Patent Publication Number: US-11665105-B2

Title: Policy-based resource-exchange life-cycle in an automated resource-exchange system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of application Ser. No. 15/285,355, filed Oct. 4, 2016, which claims the benefit of Provisional Application No. 62/380,450, filed Aug. 28, 2016. 
    
    
     TECHNICAL FIELD 
     The current document is directed to distributed computer systems, distributed-computer-system management subsystems, and, in particular, to an automated resource-exchange system that mediates sharing of computational resources among computing facilities and that uses a policy-based resource-exchange context to organize and track operations that together carry out a resource exchange within the automated resource-exchange system and to maintain state information that defines a well-defined resource-exchange life cycle. 
     BACKGROUND 
     Computer systems and computational technologies have steadily evolved, during the past 70 years, from initial vacuum-tube-based systems that lacked operating systems, compilers, network connectivity, and most other common features of modern computing systems to vast distributed computing systems that include large numbers of multi-processor servers, data-storage appliances, and multiple layers of internal communications networks interconnected by various types of wide-area networks and that provide computational resources to hundreds, thousands, tens of thousands, or more remote users. As operating systems, and virtualization layers have been developed and refined, over the years, in parallel with the advancements in computer hardware and networking, the robust execution environments provided by distributed operating systems and virtualization layers now provide a foundation for development and evolution of many different types of distributed application programs, including distributed database-management systems, distributed client-server applications, and distributed web-based service-provision applications. This has resulted in a geometric increase in the complexity of distributed computer systems, as a result of which owners, administrators, and users of distributed computer systems and consumers of computational resources provided by distributed computing systems increasingly rely on automated and semi-automated management and computational-resource-distribution subsystems to organize the activities of many users and computational-resource consumers and to control access to, and use of, computational resources within distributed computer systems. In many cases, greater overall computational efficiency can be obtained for a large number of distributed computing facilities when resources can be shared and exchanged among the distributed computing facilities. However, currently, effective resource sharing and exchange among computing facilities of multiple organizations is generally difficult or impossible. 
     SUMMARY 
     The current document is directed a resource-exchange system that facilitates resource exchange and sharing among computing facilities. The currently disclosed methods and systems employ efficient, distributed-search methods and subsystems within distributed computer systems that include large numbers of geographically distributed data centers to locate resource-provider computing facilities that match the resource needs of resource-consumer computing-facilities based on attribute values associated with the needed resources, the resource providers, and the resource consumers. The resource-exchange system organizes and tracks operations related to a resource exchange using a resource-exchange context. In one implementation, each resource-exchange context represents the stages of, and information related to, placement of one or more computational-resources-consuming entities on behalf of a resource consumer within a resource-provider computing facility and execution of the one or more computational-resources-consuming entities within the resource-provider computing facility. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS.  1 ,  2 A -E, and - 3  illustrate the problem domain addressed by the methods and systems disclosed in the current document. 
         FIG.  4    provides a general architectural diagram for various types of computers. 
         FIG.  5    illustrates an Internet-connected distributed computer system. 
         FIG.  6    illustrates cloud computing. 
         FIG.  7    illustrates generalized hardware and software components of a general-purpose computer system, such as a general-purpose computer system having an architecture similar to that shown in  FIG.  1   . 
         FIGS.  8 A-D  illustrate several types of virtual machine and virtual-machine execution environments. 
         FIG.  9    illustrates an OVF package. 
         FIG.  10    illustrates virtual data centers provided as an abstraction of underlying physical-data-center hardware components. 
         FIG.  11    illustrates virtual-machine components of a VI-management-server and physical servers of a physical data center above which a virtual-data-center interface is provided by the VI-management-server. 
         FIG.  12    illustrates a cloud-director level of abstraction. 
         FIG.  13    illustrates virtual-cloud-connector nodes (“VCC nodes”) and a VCC server, components of a distributed system that provides multi-cloud aggregation and that includes a cloud-connector server and cloud-connector nodes that cooperate to provide services that are distributed across multiple clouds. 
         FIGS.  14 A-C  illustrate components and general operation of the distributed-search methods and subsystems. 
         FIGS.  15 A-C  illustrate certain of the information and data entities used within the distributed-search methods and subsystems. 
         FIGS.  16 A-B  illustrate certain types of data maintained and used within local instances of the distributed-search subsystem and within a distributed-search engine. 
         FIG.  17    is a high-level diagram of the distributed-search engine. 
         FIG.  18    illustrates various messages and data structures used during execution of a distributed search by the distributed-search subsystem, including an active search context, a search request, a search-request response, and information requests and responses. 
         FIGS.  19 A-B  illustrate operation of the evaluator queues and master queue within an active search context. 
         FIGS.  20 A-E  illustrate the concept of resource exchange among cloud-computing facilities, data centers, and other computing facilities. 
         FIGS.  21 A-B  illustrate implementation of the automated computational-resource brokerage within multiple distributed computing facilities. 
         FIG.  22    illustrates a general implementation of the cloud-exchange engine ( 2105  in  FIG.  21 B ). 
         FIGS.  23 A-G  illustrate, for one implementation of the resource-exchange system, the process by which resource needs are communicated from resource consumers to resource providers, resource providers offer resources to resource consumers, one or more resource providers are selected for provision of particular resources, and resources are allocated on behalf of resource consumers by the one or more selected resource providers. 
         FIGS.  24 A-C  show the states associated with a resource exchange, and the transitions between the states, that define the VM placement and execution process for the described implementation of the cloud-exchange system and that define the lifecycle of a resource-exchange context and the particular resource exchange represented by the resource-exchange context. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     The current document is directed to a resource exchange that facilitates resource sharing among multiple computing facilities. In a first subsection, below, an overview of the problem domain addressed by the currently disclosed methods and systems is provided in a first subsection. A second subsection provides an overview of computer systems, virtualization layers, and distributed computer systems. A third subsection describes as distributed search engine and a fourth subsection provides a brief description of a distributed resource-exchange system that employs the distributed search engine and that aggregates a large number of physical and virtual data centers to create a distributed, multi-organization computing, resource-exchange, and resource-sharing facility. Finally, a fifth subsection discusses the life cycle of a resource exchange as represented by a resource-exchange context. 
     The Problem Domain Addressed by the Currently Disclosed Methods and Systems 
       FIGS.  1 - 3    illustrate the problem domain addressed by the methods and systems disclosed in the current document.  FIG.  1    shows a large number of virtual and physical data centers spread throughout a large geographical area. Each virtual/physical data center may include hundreds to thousands of individual computer systems along with internal networking and pooled mass-storage resources. Although only 30 virtual/physical data centers are shown in  FIG.  1   , hundreds to thousands of virtual/physical data centers may be spread throughout a large geographical area. As shown in  FIG.  1   , the virtual/physical data centers are connected to regional communications hubs  102 - 107 , which are, in turn, interconnected through wide-area networking  108 . Each virtual/physical data center is represented by a rectangle, such as virtual/physical data center  110 . Each rectangle representing a virtual/physical data center is additionally labeled with an indication of the organization that owns and maintains the virtual/physical data center, such as the indication “O 1 ” within the rectangle representing virtual/physical data center  110 . Certain organizations own and maintain only a single virtual/physical data center, including organization “O 18 ,” which owns and maintains virtual/physical data center  112 . Other organizations own and maintain multiple virtual/physical data centers, including organization “O 1 ,” which owns and maintains virtual/physical data centers  110  and  114 - 116 . 
     Currently, an organization can supplement the computational resources of the organization&#39;s one or more virtual/physical data centers by contracting for computational resources from cloud-computing facilities. An organization can configure virtual machines within a cloud-computing facility to remotely run applications and services on behalf of the organization. Use of computational resources provided by cloud-computing facilities allows an organization to expand and contract computational resources in response to increasing and decreasing demand for the services provided by the organization, without purchasing additional physical computer systems to satisfy increased demand and without powering down physical computer systems to lessen ongoing costs associated with spare capacity. The advent of cloud computing has enabled organizations to make use of flexible and dynamic remote computational resources to obtain needed computational resources without needing to purchase, maintain, and manage additional computational resources on-site. However, third-party cloud-computing facilities do not fully address the computational-resource needs of organizations, fail to address the recurring problem of spare capacity within private virtual/physical data centers, and fail to provide seamless migration of virtual machines back and forth between resource consumers and resource providers as well as seamless extension of a resource-consumer&#39;s private virtual-machine execution environment into the cloud-based domain of resource providers. 
     It should be emphasized that the problem domain addressed by the currently disclosed methods and systems is, in general, one of computational efficiency. As discussed below, the automated resource-exchange system, in which the currently disclosed methods and systems are employed, facilitates sharing and exchange of computational resources among very large numbers of virtual/physical data centers that are owned, maintained, and managed by large numbers of different organizations. The resource-exchange system effectively aggregates portions of the computational resources of the large number of virtual/physical data centers for use by organizations in need of additional computational resources. As a result, the large numbers of virtual/physical data centers, as a whole, can achieve significantly greater computational efficiencies through resource exchange and sharing. In other words, the resource-exchange system provides a means for partially aggregating multiple virtual/physical data centers and for increasing the computational efficiency of the partially aggregated virtual/physical data centers. 
     In the implementations discussed in the current application, the resource-exchange system partially aggregates multiple virtual/physical data centers by providing a largely automated auction-based marketplace in which computational resources are advertised for lease by resource sellers and leased from resource sellers by resource buyers. In other words, the resource-exchange system achieves computational efficiencies through computational-resource transactions. In the described implementations, these transactions involve financial exchanges between buyers and sellers. However, the financial exchanges are used to simplify the complex problems associated with matching buyers to sellers and sellers to buyers. Similar computational efficiencies can be alternatively obtained using more abstract credit exchanges, rather than financial exchanges or by directly trading different types of computational resources and services. However, since many of the various considerations and constraints associated with leasing computational resources and with other types of resource exchanges are naturally expressed in terms of financial costs and benefits, use of financial exchanges represents a significant computational efficiency for the resource-exchange system. The primary goal for creating and operating the resource-exchange system is, despite the use of financial transactions, to increase the overall efficiencies related to owning, maintaining, and the managing virtual/physical data centers rather than to create a new type of financial market. 
       FIGS.  2 A-E  illustrate an example of a cost-efficiency increase for a virtual/physical data center made possible by the resource-exchange system. In  FIG.  2 A , the virtual/physical data center  202  is represented as a large rectangle containing numerous physical server computers, including server  204 . In  FIGS.  2 A-E , multiple ellipses, such as ellipses  206 , are used to indicate that a particular row of servers includes many additional servers not explicitly shown in the figures. In the numerical examples that follow, each of the ellipses represents seven servers that are not shown in the figures. Each server, including server  204 , is generally shown as including a first unshaded portion, such as portion  208  of server  204 , representing unused server resources and a second shaded portion, such as second portion  210 , representing currently used server resources. Server  204  is currently being used at 80% of the server&#39;s total capacity. In this example, servers are generally loaded to 80% capacity. In the example of  FIGS.  2 A-E , the organization managing the virtual/physical data center  202  intends to purchase an additional 10 servers due to an expected low price point for servers. Three different strategies for purchasing the 10 additional servers are shown, in  FIGS.  2 A-B , as strategies A  212 , B  214 , and C  216 . 
     According to strategy A, the 10 additional servers  220 - 222  are immediately purchased and installed in the virtual/physical data center  212 . Tasks running within the virtual/physical data center  212  are redistributed among the now 40 servers running within the virtual/physical data center. Redistribution of the tasks lowers the use of each server to 60% of capacity, as can be seen by comparing the size of the unshaded portion  224  and shaded portion  226  of server  204  in the virtual/physical data center illustrating strategy A  212  to the unshaded portion  208  and shaded portion  210  of server  204  in the initial  30 -server virtual/physical data center  202 . 
     Purchasing the 10 additional servers according to strategy B involves immediately purchasing the 10 additional servers  230 - 232  but leaving them powered down until there is additional demand within the virtual/physical data center for additional computational resources. Purchasing the 10 additional servers according to strategy C involves purchasing one additional server  234  and waiting to purchase a second additional server  235  until the first additional server  234  approaches use at 80% of capacity. 
       FIG.  2 C  illustrates the costs incurred at successive time points by the organization when additional servers are purchased according to strategies A, B, and C. The cost calculations are approximate and based on a coarse, 5-day granularity, but nonetheless relative accurately illustrate the cost implications of the three different strategies. For this simple example, there are four different types of costs associated with acquiring and running servers: (1) the cost of running a server  236 , which includes power and maintenance costs, estimated at five dollars per day; (2) the cost of housing the server within the data center  237 , estimated to be 1 dollar per day; (3) the cost of purchasing a new server  238 , $800 at time t 1  ( 239  in table  240 ), with purchase-cost increases at subsequent time intervals shown in table  240 ; and (4) the cost of installing a server in the data center  241 , estimated at $200 for installing a single server  242 , but less per server as the number of servers installed at a single time point increases, as shown in table  243 . In the current example, each interval between successive time points represents five days  244 . The initial system includes 30 servers  245  and thus incurs a cost of $150 per day to run the servers and a cost of $30 per day to house the servers. In the lower portion of  FIG.  2 C   246 , the accumulated costs for the data center at successive intervals t 1 , t 2 , t 6  are shown for strategy A  247 , strategy B  248 , and strategy C  249 . These costs assume that the purchase of the 10 additional servers begins at time point t 1 , 5 days following an initial time point t 0 . For strategy A, at time point t 1 , the cost for running the 40 servers  250  is $200 per day, the cost for housing the servers  251  is $40 per day, the cost for purchasing the 10 additional servers  252  is $8000, according to table  240 , and the cost of installing the 10 additional servers  253  is $1400, according to table  243 . The total cost accumulated since time point t 0    253  is $900, which is the cost of running the initial virtual/physical data center  202  per day, $180, multiplied by 5 days. For strategy A at time point t 2 , the total cost accumulated since time point t 0    255  is $11,500, which includes the total cost  254  of $900 accumulated up to time point t 1  along with the price of purchasing and installing the 10 additional servers and 5 times the daily cost of running the servers, $240×5=$1200. As shown in  FIG.  2 C , by time point t 6 , the total accumulated cost  256  of strategy A is $16,300, the total accumulated cost  257  of strategy B is $15,300, and the total accumulated cost  258  of strategy C is $12,400. However, the rate of increase in total-accumulated-cost for strategy C is much steeper than those for strategies A and B. 
       FIG.  2 D  illustrates a fourth strategy D for purchasing the 10 additional servers made possible by the resource-exchange system. According to the fourth strategy D, the 10 additional servers  260 - 262  are immediately purchased and installed. However, rather than redistributing tasks within the virtual/physical data center, as in strategy A, the organization managing virtual/physical data center  202  advertises the availability of computational-resource leases to other organizations participating in the marketplace provided by the resource-exchange system. As a result, within a reasonably short period of time, the new additional servers are operating at 80% of capacity  263 - 2652  executing virtual machines on behalf of remote computational-resource leasing organizations. Because the organization managing virtual/physical data center  202  is leasing the 10 additional servers, there is a negative cost, or revenue  266 , associated with the 10 additional servers. Using the same illustration conventions as used in  FIG.  2 C , the costs associated with strategy D are shown at successive time points  267 - 271 . By comparing these costs to those for strategies A, B, and C, shown in  FIG.  2 C , the rate of increase in total-accumulated-cost for strategy D is much flatter than those for strategies A, B, and C. 
       FIG.  2 E  shows a plot of the total accumulated cost vs. time for the four strategies A, B, C, and D, discussed above with reference to  FIGS.  2 A-D . Clearly, after less than 30 days, strategy D, represented by cost curve  272 , provides a significantly lower accumulated cost then strategies A, B, and C, represented by cost curves  273 - 275 . The resource-exchange system has provided a way for the organization managing virtual/physical data center  202  to maximize use of the computational resources within the virtual/physical data center and, by doing so, minimize operating costs. In addition, the organizations that lease computational resources provided by the 10 additional servers also achieve access to greater computational bandwidth for far less cost than would be incurred by purchasing and installing new physical servers. Considering the data centers participating in the market provided by the resource-exchange system as a large computing-facility aggregation, the aggregate computational efficiency is much higher, when leasing transactions are automatically facilitated by the resource-exchange system, than when no resource exchanges are possible. In the example discussed above with reference to  FIGS.  2 A-E , a larger fraction of the aggregate computational resources of the data centers are used because additional tasks are being executed by the 10 additional servers. Eventually, the 10 additional servers in data center  202  may be used for executing tasks on behalf of the organization that manages virtual/physical data center  202 , once the leases have terminated. But, by initially purchasing the 10 additional servers at time point t 1 , the organization managing data center  202  has taken advantage of a favorable purchase price for the 10 additional servers at time point t 1  without bearing the cost of the spare capacity represented by the 10 additional servers until internal tasks become available. 
       FIG.  3    illustrates another example of how the resource-exchange system can increase the computational efficiency of an aggregation of virtual/physical data centers. At the top of  FIG.  3   , two virtual/physical data centers  302  and  304  are shown as large rectangles. Indications  306  and  308  of the currently available computational resources within the virtual/physical data centers  302  and  304  are shown within the rectangles representing virtual/physical data centers  302  and  304 . These resources include CPU bandwidth, available memory, and available mass-storage, in appropriate units. The first virtual/physical data center  302  is shown receiving a request  310  to execute an additional task, implemented as a virtual machine, that requires 10 units of CPU bandwidth, 4 units of memory, and 100 units of mass storage. The first virtual/physical data center declines  312  the request because the first virtual/physical data center has insufficient storage resources for executing the virtual machine. Similarly, the second virtual/physical data center  304  receives a request  314  to execute a new virtual machine, but declines  316  the request because the second data lacks sufficient CPU bandwidth to execute the new virtual machine. 
     The same two virtual/physical data centers  302  and  304  and the same two virtual-machine-execution requests  310  and  314  are again shown in the lower portion of  FIG.  3   . However, in the example shown in the lower portion of  FIG.  3   , the two data centers have exchanged two already executing virtual machines  320  and  322  via the marketplace provided by the resource-exchange system. The virtual/physical first data center  302  has leased computational resources from the second virtual/physical data center  304  to execute a storage-intensive virtual machine  320 . Because the second virtual/physical data center has an excess of mass-storage resources, the second virtual/physical data center can host virtual machine  320  less expensively than the virtual machine can be executed within the first virtual/physical data center  302 . Similarly, the second data center has leased computational resources from the first virtual/physical data center to execute the CPU-bandwidth-intensive virtual machine  322 . The result of exchanging virtual machines  320  and  322  is a decrease in the operational costs for both data centers and more balanced ratios of different types of available computational resources within each virtual/physical data center. As a result, the first virtual/physical data center  302  can now accept  324  the virtual-machine-execution request  310  and the second virtual/physical data center  304  can now except  326  the virtual-machine-execution request  314 . Thus, due to ongoing computational-resource exchanges made possible by the resource-exchange system, the partial aggregation of the two data centers can run more tasks, with greater overall capacity usage, than in the case that resource exchanges are not possible. The partial aggregation of the two virtual/physical data centers is significantly more computationally efficient because of their use of the marketplace provided by the resource-exchange system. 
     Thus, although the resource-exchange system is discussed in terms of providing a computational-resource-leasing marketplace, the resource-exchange system is an effective tool for increasing the computational efficiency of a partial aggregation of multiple data centers or multiple clusters within a datacenter. The resource-exchange system functions to increase the fraction of resource-capacity usage in the partial aggregation of multiple data centers as well as to redistribute load in order to balance the ratios of different available computational resources used within each data center to facilitate execution of additional task load. 
     Overview of Computer Systems and Computer Architecture 
       FIG.  4    provides a general architectural diagram for various types of computers. The computer system contains one or multiple central processing units (“CPUs”)  402 - 405 , one or more electronic memories  408  interconnected with the CPUs by a CPU/memory-subsystem bus  410  or multiple busses, a first bridge  412  that interconnects the CPU/memory-subsystem bus  410  with additional busses  414  and  416 , or other types of high-speed interconnection media, including multiple, high-speed serial interconnects. These busses or serial interconnections, in turn, connect the CPUs and memory with specialized processors, such as a graphics processor  418 , and with one or more additional bridges  420 , which are interconnected with high-speed serial links or with multiple controllers  422 - 427 , such as controller  427 , that provide access to various different mass-storage devices  428 , electronic displays, input devices, and other such components, subcomponents, and computational resources. It should be noted that computer-readable data-storage devices include optical and electromagnetic disks, electronic memories, and other physical data-storage devices. Those familiar with modern science and technology appreciate that electromagnetic radiation and propagating signals do not store data for subsequent retrieval, and can transiently “store” only a byte or less of information per mile, far less information than needed to encode even the simplest of routines. 
     Of course, there are many different types of computer-system architectures that differ from one another in the number of different memories, including different types of hierarchical cache memories, the number of processors and the connectivity of the processors with other system components, the number of internal communications busses and serial links, and in many other ways. However, computer systems generally execute stored programs by fetching instructions from memory and executing the instructions in one or more processors. Computer systems include general-purpose computer systems, such as personal computers (“PCs”), various types of servers and workstations, and higher-end mainframe computers, but may also include a plethora of various types of special-purpose computing devices, including data-storage systems, communications routers, network nodes, tablet computers, and mobile telephones. 
       FIG.  5    illustrates an Internet-connected distributed computer system. As communications and networking technologies have evolved in capability and accessibility, and as the computational bandwidths, data-storage capacities, and other capabilities and capacities of various types of computer systems have steadily and rapidly increased, much of modern computing now generally involves large distributed systems and computers interconnected by local networks, wide-area networks, wireless communications, and the Internet.  FIG.  5    shows a typical distributed system in which a large number of PCs  502 - 505 , a high-end distributed mainframe system  510  with a large data-storage system  512 , and a large computer center  514  with large numbers of rack-mounted servers or blade servers all interconnected through various communications and networking systems that together comprise the Internet  516 . Such distributed computer systems provide diverse arrays of functionalities. For example, a PC user sitting in a home office may access hundreds of millions of different web sites provided by hundreds of thousands of different web servers throughout the world and may access high-computational-bandwidth computing services from remote computer facilities for running complex computational tasks. 
     Until recently, computational services were generally provided by computer systems and data centers purchased, configured, managed, and maintained by service-provider organizations. For example, an e-commerce retailer generally purchased, configured, managed, and maintained a data center including numerous web servers, back-end computer systems, and data-storage systems for serving web pages to remote customers, receiving orders through the web-page interface, processing the orders, tracking completed orders, and other myriad different tasks associated with an e-commerce enterprise. 
       FIG.  6    illustrates cloud computing. In the recently developed cloud-computing paradigm, computing cycles and data-storage facilities are provided to organizations and individuals by cloud-computing providers. In addition, larger organizations may elect to establish private cloud-computing facilities in addition to, or instead of, subscribing to computing services provided by public cloud-computing service providers. In  FIG.  6   , a system administrator for an organization, using a PC  602 , accesses the organization&#39;s private cloud  604  through a local network  606  and private-cloud interface  608  and also accesses, through the Internet  610 , a public cloud  612  through a public-cloud services interface  614 . The administrator can, in either the case of the private cloud  604  or public cloud  612 , configure virtual computer systems and even entire virtual data centers and launch execution of application programs on the virtual computer systems and virtual data centers in order to carry out any of many different types of computational tasks. As one example, a small organization may configure and run a virtual data center within a public cloud that executes web servers to provide an e-commerce interface through the public cloud to remote customers of the organization, such as a user viewing the organization&#39;s e-commerce web pages on a remote user system  616 . 
       FIG.  7    illustrates generalized hardware and software components of a general-purpose computer system, such as a general-purpose computer system having an architecture similar to that shown in  FIG.  4   . The computer system  700  is often considered to include three fundamental layers: (1) a hardware layer or level  702 ; (2) an operating-system layer or level  704 ; and (3) an application-program layer or level  706 . The hardware layer  702  includes one or more processors  708 , system memory  710 , various input-output (“I/O”) devices  710  and  712 , and mass-storage devices  714 . Of course, the hardware level also includes many other components, including power supplies, internal communications links and busses, specialized integrated circuits, many different types of processor-controlled or microprocessor-controlled peripheral devices and controllers, and many other components. The operating system  704  interfaces to the hardware level  702  through a low-level operating system and hardware interface  716  generally comprising a set of non-privileged computer instructions  718 , a set of privileged computer instructions  720 , a set of non-privileged registers and memory addresses  722 , and a set of privileged registers and memory addresses  724 . In general, the operating system exposes non-privileged instructions, non-privileged registers, and non-privileged memory addresses  726  and a system-call interface  728  as an operating-system interface  730  to application programs  732 - 736  that execute within an execution environment provided to the application programs by the operating system. The operating system, alone, accesses the privileged instructions, privileged registers, and privileged memory addresses. By reserving access to privileged instructions, privileged registers, and privileged memory addresses, the operating system can ensure that application programs and other higher-level computational entities cannot interfere with one another&#39;s execution and cannot change the overall state of the computer system in ways that could deleteriously impact system operation. The operating system includes many internal components and modules, including a scheduler  742 , memory management  744 , a file system  746 , device drivers  748 , and many other components and modules. To a certain degree, modern operating systems provide numerous levels of abstraction above the hardware level, including virtual memory, which provides to each application program and other computational entities a separate, large, linear memory-address space that is mapped by the operating system to various electronic memories and mass-storage devices. The scheduler orchestrates interleaved execution of various application programs and higher-level computational entities, providing to each application program a virtual, stand-alone system devoted entirely to the application program From the application program&#39;s standpoint, the application program executes continuously without concern for the need to share processor resources and other system resources with other application programs and higher-level computational entities. The device drivers abstract details of hardware-component operation, allowing application programs to employ the system-call interface for transmitting and receiving data to and from communications networks, mass-storage devices, and other I/O devices and subsystems. The file system  746  facilitates abstraction of mass-storage-device and memory resources as a high-level, easy-to-access, file-system interface. 
     In many modern operating systems, the operating system provides an execution environment for concurrent execution of a large number of processes, each corresponding to an executing application program, on one or a relatively small number of hardware processors by temporal multiplexing of process execution. Thus, the development and evolution of the operating system has resulted in the generation of a type of multi-faceted virtual execution environment for application programs and other higher-level computational entities. 
     While the execution environments provided by operating systems have proved to be an enormously successful level of abstraction within computer systems, the operating-system-provided level of abstraction is nonetheless associated with difficulties and challenges for developers and users of application programs and other higher-level computational entities. One difficulty arises from the fact that there are many different operating systems that run within various different types of computer hardware. In many cases, popular application programs and computational systems are developed to run on only a subset of the available operating systems, and can therefore be executed within only a subset of the various different types of computer systems on which the operating systems are designed to run. Often, even when an application program or other computational system is ported to additional operating systems, the application program or other computational system can nonetheless run more efficiently on the operating systems for which the application program or other computational system was originally targeted. Another difficulty arises from the increasingly distributed nature of computer systems. Although distributed operating systems are the subject of considerable research and development efforts, many of the popular operating systems are designed primarily for execution on a single computer system. In many cases, it is difficult to move application programs, in real time, between the different computer systems of a distributed computer system for high-availability, fault-tolerance, and load-balancing purposes. The problems are even greater in heterogeneous distributed computer systems which include different types of hardware and devices running different types of operating systems. Operating systems continue to evolve, as a result of which certain older application programs and other computational entities may be incompatible with more recent versions of operating systems for which they are targeted, creating compatibility issues that are particularly difficult to manage in large distributed systems. 
     For these reasons, a higher level of abstraction, referred to as the “virtual machine,” has been developed and evolved to further abstract computer hardware in order to address many difficulties and challenges associated with traditional computing systems, including the compatibility issues discussed above.  FIGS.  8 A-B  illustrate two types of virtual machine and virtual-machine execution environments.  FIGS.  8 A-B  use the same illustration conventions as used in  FIG.  7   .  FIG.  8 A  shows a first type of virtualization. The computer system  800  in  FIG.  8 A  includes the same hardware layer  802  as the hardware layer  702  shown in  FIG.  7   . However, rather than providing an operating system layer directly above the hardware layer, as in  FIG.  7   , the virtualized computing environment illustrated in  FIG.  8 A  features a virtualization layer  804  that interfaces through a virtualization-layer/hardware-layer interface  806 , equivalent to interface  716  in  FIG.  7   , to the hardware. The virtualization layer provides a hardware-like interface  808  to a number of virtual machines, such as virtual machine  810 , executing above the virtualization layer in a virtual-machine layer  812 . Each virtual machine includes one or more application programs or other higher-level computational entities packaged together with an operating system, referred to as a “guest operating system,” such as application  814  and guest operating system  816  packaged together within virtual machine  810 . Each virtual machine is thus equivalent to the operating-system layer  704  and application-program layer  706  in the general-purpose computer system shown in  FIG.  7   . Each guest operating system within a virtual machine interfaces to the virtualization-layer interface  808  rather than to the actual hardware interface  806 . The virtualization layer partitions hardware resources into abstract virtual-hardware layers to which each guest operating system within a virtual machine interfaces. The guest operating systems within the virtual machines, in general, are unaware of the virtualization layer and operate as if they were directly accessing a true hardware interface. The virtualization layer ensures that each of the virtual machines currently executing within the virtual environment receive a fair allocation of underlying hardware resources and that all virtual machines receive sufficient resources to progress in execution. The virtualization-layer interface  808  may differ for different guest operating systems. For example, the virtualization layer is generally able to provide virtual hardware interfaces for a variety of different types of computer hardware. This allows, as one example, a virtual machine that includes a guest operating system designed for a particular computer architecture to run on hardware of a different architecture. The number of virtual machines need not be equal to the number of physical processors or even a multiple of the number of processors. 
     The virtualization layer includes a virtual-machine-monitor module  818  (“VMM”) that virtualizes physical processors in the hardware layer to create virtual processors on which each of the virtual machines executes. For execution efficiency, the virtualization layer attempts to allow virtual machines to directly execute non-privileged instructions and to directly access non-privileged registers and memory. However, when the guest operating system within a virtual machine accesses virtual privileged instructions, virtual privileged registers, and virtual privileged memory through the virtualization-layer interface  808 , the accesses result in execution of virtualization-layer code to simulate or emulate the privileged resources. The virtualization layer additionally includes a kernel module  820  that manages memory, communications, and data-storage machine resources on behalf of executing virtual machines (“VM kernel”). The VM kernel, for example, maintains shadow page tables on each virtual machine so that hardware-level virtual-memory facilities can be used to process memory accesses. The VM kernel additionally includes routines that implement virtual communications and data-storage devices as well as device drivers that directly control the operation of underlying hardware communications and data-storage devices. Similarly, the VM kernel virtualizes various other types of I/O devices, including keyboards, optical-disk drives, and other such devices. The virtualization layer essentially schedules execution of virtual machines much like an operating system schedules execution of application programs, so that the virtual machines each execute within a complete and fully functional victual hardware layer. 
       FIG.  8 B  illustrates a second type of virtualization. In  FIG.  8 B , the computer system  840  includes the same hardware layer  842  and software layer  844  as the hardware layer  702  shown in  FIG.  7   . Several application programs  846  and  848  are shown running in the execution environment provided by the operating system. In addition, a virtualization layer  850  is also provided, in computer  840 , but, unlike the virtualization layer  804  discussed with reference to  FIG.  8 A , virtualization layer  850  is layered above the operating system  844 , referred to as the “host OS,” and uses the operating system interface to access operating-system-provided functionality as well as the hardware. The virtualization layer  850  comprises primarily a VMM and a hardware-like interface  852 , similar to hardware-like interface  808  in  FIG.  8 A . The virtualization-layer/hardware-layer interface  852 , similar to interface  716  in  FIG.  7   , provides an execution environment for a number of virtual machines  856 - 858 , each including one or more application programs or other higher-level computational entities packaged together with a guest operating system. 
     In  FIGS.  8 A-B , the layers are somewhat simplified for clarity of illustration. For example, portions of the virtualization layer  850  may reside within the host-operating-system kernel, such as a specialized driver incorporated into the host operating system to facilitate hardware access by the virtualization layer. 
     While the traditional virtual-machine-based virtualization layers, described with reference to  FIGS.  8 A-B , have enjoyed widespread adoption and use in a variety of different environments, from personal computers to enormous distributed computing systems, traditional virtualization technologies are associated with computational overheads. While these computational overheads have been steadily decreased, over the years, and often represent ten percent or less of the total computational bandwidth consumed by an application running in a virtualized environment, traditional virtualization technologies nonetheless involve computational costs in return for the power and flexibility that they provide. Another approach to virtualization is referred to as operating-system-level virtualization (“OSL virtualization”).  FIG.  8 C  illustrates the OSL-virtualization approach. In  FIG.  8 C , as in previously discussed  FIG.  7   , an operating system  704  runs above the hardware  702  of a host computer. The operating system provides an interface for higher-level computational entities, the interface including a system-call interface  728  and exposure to the non-privileged instructions and memory addresses and registers  726  of the hardware layer  702 . However, unlike in  FIG.  8 A , rather than applications running directly above the operating system, OSL virtualization involves an OS-level virtualization layer  860  that provides an operating-system interface  862 - 864  to each of one or more containers  866 - 868 . The containers, in turn, provide an execution environment for one or more applications, such as application  870  running within the execution environment provided by container  866 . The container can be thought of as a partition of the resources generally available to higher-level computational entities through the operating system interface  730 . While a traditional virtualization layer can simulate the hardware interface expected by any of many different operating systems, OSL virtualization essentially provides a secure partition of the execution environment provided by a particular operating system. As one example, OSL virtualization provides a file system to each container, but the file system provided to the container is essentially a view of a partition of the general file system provided by the underlying operating system. In essence, OSL virtualization uses operating-system features, such as name space support, to isolate each container from the remaining containers so that the applications executing within the execution environment provided by a container are isolated from applications executing within the execution environments provided by all other containers. As a result, a container can be booted up much faster than a virtual machine, since the container uses operating-system-kernel features that are already available within the host computer. Furthermore, the containers share computational bandwidth, memory, network bandwidth, and other computational resources provided by the operating system, without resource overhead allocated to virtual machines and virtualization layers. Again, however, OSL virtualization does not provide many desirable features of traditional virtualization. As mentioned above, OSL virtualization does not provide a way to run different types of operating systems for different groups of containers within the same host system, nor does OSL-virtualization provide for live migration of containers between host computers, as does traditional virtualization technologies. 
       FIG.  8 D  illustrates an approach to combining the power and flexibility of traditional virtualization with the advantages of OSL virtualization.  FIG.  8 D  shows a host computer similar to that shown in  FIG.  8 A , discussed above. The host computer includes a hardware layer  802  and a virtualization layer  804  that provides a simulated hardware interface  808  to an operating system  872 . Unlike in  FIG.  8 A , the operating system interfaces to an OSL-virtualization layer  874  that provides container execution environments  876 - 878  to multiple application programs. Running containers above a guest operating system within a virtualized host computer provides many of the advantages of traditional virtualization and OSL virtualization. Containers can be quickly booted in order to provide additional execution environments and associated resources to new applications. The resources available to the guest operating system are efficiently partitioned among the containers provided by the OSL-virtualization layer  874 . Many of the powerful and flexible features of the traditional virtualization technology can be applied to containers running above guest operating systems including live migration from one host computer to another, various types of high-availability and distributed resource sharing, and other such features. Containers provide share-based allocation of computational resources to groups of applications with guaranteed isolation of applications in one container from applications in the remaining containers executing above a guest operating system. Moreover, resource allocation can be modified at run time between containers. The traditional virtualization layer provides flexible and easy scaling and a simple approach to operating-system upgrades and patches. Thus, the use of OSL virtualization above traditional virtualization, as illustrated in  FIG.  8 D , provides much of the advantages of both a traditional virtualization layer and the advantages of OSL virtualization. Note that, although only a single guest operating system and OSL virtualization layer as shown in  FIG.  8 D , a single virtualized host system can run multiple different guest operating systems within multiple virtual machines, each of which supports one or more containers. 
     In  FIGS.  8 A-D , the layers are somewhat simplified for clarity of illustration. For example, portions of the virtualization layer  850  may reside within the host-operating-system kernel, such as a specialized driver incorporated into the host operating system to facilitate hardware access by the virtualization layer. 
     It should be noted that virtual hardware layers, virtualization layers, and guest operating systems are all physical entities that are implemented by computer instructions stored in physical data-storage devices, including electronic memories, mass-storage devices, optical disks, magnetic disks, and other such devices. The term “virtual” does not, in any way, imply that virtual hardware layers, virtualization layers, and guest operating systems are abstract or intangible. Virtual hardware layers, virtualization layers, and guest operating systems execute on physical processors of physical computer systems and control operation of the physical computer systems, including operations that alter the physical states of physical devices, including electronic memories and mass-storage devices. They are as physical and tangible as any other component of a computer since, such as power supplies, controllers, processors, busses, and data-storage devices. 
     A virtual machine or virtual application, described below, is encapsulated within a data package for transmission, distribution, and loading into a virtual-execution environment. One public standard for virtual-machine encapsulation is referred to as the “open virtualization format” (“OVF”). The OVF standard specifies a format for digitally encoding a virtual machine within one or more data files.  FIG.  9    illustrates an OVF package. An OVF package  902  includes an OVF descriptor  904 , an OVF manifest  906 , an OVF certificate  908 , one or more disk-image files  910 - 911 , and one or more resource files  912 - 914 . The OVF package can be encoded and stored as a single file or as a set of files. The OVF descriptor  904  is an XML document  920  that includes a hierarchical set of elements, each demarcated by a beginning tag and an ending tag. The outermost, or highest-level, element is the envelope element, demarcated by tags  922  and  923 . The next-level element includes a reference element  926  that includes references to all files that are part of the OVF package, a disk section  928  that contains meta information about the virtual disks included in the OVF package, a networks section  930  that includes meta information about the logical networks included in the OVF package, and a collection of virtual-machine configurations  932  which further includes hardware descriptions of each virtual machine  934 . There are many additional hierarchical levels and elements within a typical OVF descriptor. The OVF descriptor is thus a self-describing XML file that describes the contents of an OVF package. The OVF manifest  906  is a list of cryptographic-hash-function-generated digests  936  of the entire OVF package and of the various components of the OVF package. The OVF certificate  908  is an authentication certificate  940  that includes a digest of the manifest and that is cryptographically signed. Disk image files, such as disk image file  910 , are digital encodings of the contents of victual disks and resource files  912  are digitally encoded content, such as operating-system images. A virtual machine or a collection of virtual machines encapsulated together within a virtual application can thus be digitally encoded as one or more files within an OVF package that can be transmitted, distributed, and loaded using well-known tools for transmitting, distributing, and loading files. A virtual appliance is a software service that is delivered as a complete software stack installed within one or more virtual machines that is encoded within an OVF package. 
       FIG.  10    illustrates virtual data centers provided as an abstraction of underlying physical-data-center hardware components. In  FIG.  10   , a physical data center  1002  is shown below a virtual-interface plane  1004 . The physical data center consists of a virtual-infrastructure management server (“VI-management-server”)  1006  and any of various different computers, such as PCs  1008 , on which a virtual-data-center management interface may be displayed to system administrators and other users. The physical data center additionally includes generally large numbers of server computers, such as server computer  1010 , that are coupled together by local area networks, such as local area network  1012  that directly interconnects server computer  1010  and  1014 - 1020  and a mass-storage array  1022 . The physical data center shown in  FIG.  10    includes three local area networks  1012 ,  1024 , and  1026  that each directly interconnects a bank of eight servers and a mass-storage array. The individual server computers, such as server computer  1010 , each includes a virtualization layer and runs multiple virtual machines. Different physical data centers may include many different types of computers, networks, data-storage systems and devices connected according to many different types of connection topologies. The virtual-data-center abstraction layer  1004 , a logical abstraction layer shown by a plane in  FIG.  10   , abstracts the physical data center to a virtual data center comprising one or more resource pools, such as resource pools  1030 - 1032 , one or more virtual data stores, such as virtual data stores  1034 - 1036 , and one or more virtual networks. In certain implementations, the resource pools abstract banks of physical servers directly interconnected by a local area network. 
     The virtual-data-center management interface allows provisioning and launching of virtual machines with respect to resource pools, virtual data stores, and virtual networks, so that virtual-data-center administrators need not be concerned with the identities of physical-data-center components used to execute particular virtual machines. Furthermore, the VI-management-server includes functionality to migrate running virtual machines from one physical server to another in order to optimally or near optimally manage resource allocation, provide fault tolerance, and high availability by migrating virtual machines to most effectively utilize underlying physical hardware resources, to replace virtual machines disabled by physical hardware problems and failures, and to ensure that multiple virtual machines supporting a high-availability virtual appliance are executing on multiple physical computer systems so that the services provided by the virtual appliance are continuously accessible, even when one of the multiple virtual appliances becomes compute bound, data-access bound, suspends execution, or fails. Thus, the virtual data center layer of abstraction provides a virtual-data-center abstraction of physical data centers to simplify provisioning, launching, and maintenance of virtual machines and virtual appliances as well as to provide high-level, distributed functionalities that involve pooling the resources of individual physical servers and migrating virtual machines among physical servers to achieve load balancing, fault tolerance, and high availability. 
       FIG.  11    illustrates virtual-machine components of a VI-management-server and physical servers of a physical data center above which a virtual-data-center interface is provided by the VI-management-server. The VI-management-server  1102  and a virtual-data-center database  1104  comprise the physical components of the management component of the virtual data center. The VI-management-server  1102  includes a hardware layer  1106  and virtualization layer  1108 , and runs a virtual-data-center management-server virtual machine  1110  above the virtualization layer. Although shown as a single server in  FIG.  11   , the VI-management-server (“VI management server”) may include two or more physical server computers that support multiple VI-management-server virtual appliances. The virtual machine  1110  includes a management-interface component  1112 , distributed services  1114 , core services  1116 , and a host-management interface  1118 . The management interface is accessed from any of various computers, such as the PC  1008  shown in  FIG.  10   . The management interface allows the virtual-data-center administrator to configure a virtual data center, provision virtual machines, collect statistics and view log files for the virtual data center, and to carry out other, similar management tasks. The host-management interface  1118  interfaces to virtual-data-center agents  1124 ,  1125 , and  1126  that execute as virtual machines within each of the physical servers of the physical data center that is abstracted to a virtual data center by the VI management server. 
     The distributed services  1114  include a distributed-resource scheduler that assigns virtual machines to execute within particular physical servers and that migrates virtual machines in order to most effectively make use of computational bandwidths, data-storage capacities, and network capacities of the physical data center. The distributed services further include a high-availability service that replicates and migrates virtual machines in order to ensure that virtual machines continue to execute despite problems and failures experienced by physical hardware components. The distributed services also include a live-virtual-machine migration service that temporarily halts execution of a virtual machine, encapsulates the virtual machine in an OVF package, transmits the OVF package to a different physical server, and restarts the virtual machine on the different physical server from a virtual-machine state recorded when execution of the virtual machine was halted. The distributed services also include a distributed backup service that provides centralized virtual-machine backup and restore. 
     The core services provided by the VI management server include host configuration, virtual-machine configuration, virtual-machine provisioning, generation of virtual-data-center alarms and events, ongoing event logging and statistics collection, a task scheduler, and a resource-management module. Each physical server  1120 - 1122  also includes a host-agent virtual machine  1128 - 1130  through which the virtualization layer can be accessed via a virtual-infrastructure application programming interface (“API”). This interface allows a remote administrator or user to manage an individual server through the infrastructure API. The virtual-data-center agents  1124 - 1126  access virtualization-layer server information through the host agents. The virtual-data-center agents are primarily responsible for offloading certain of the virtual-data-center management-server functions specific to a particular physical server to that physical server. The virtual-data-center agents relay and enforce resource allocations made by the VI management server, relay virtual-machine provisioning and configuration-change commands to host agents, monitor and collect performance statistics, alarms, and events communicated to the virtual-data-center agents by the local host agents through the interface API, and to carry out other, similar virtual-data-management tasks. 
     The virtual-data-center abstraction provides a convenient and efficient level of abstraction for exposing the computational resources of a cloud-computing facility to cloud-computing-infrastructure users. A cloud-director management server exposes virtual resources of a cloud-computing facility to cloud-computing-infrastructure users. In addition, the cloud director introduces a multi-tenancy layer of abstraction, which partitions virtual data centers (“VDCs”) into tenant-associated VDCs that can each be allocated to a particular individual tenant or tenant organization, both referred to as a “tenant.” A given tenant can be provided one or more tenant-associated VDCs by a cloud director managing the multi-tenancy layer of abstraction within a cloud-computing facility. The cloud services interface ( 308  in  FIG.  3   ) exposes a virtual-data-center management interface that abstracts the physical data center. 
       FIG.  12    illustrates a cloud-director level of abstraction. In  FIG.  12   , three different physical data centers  1202 - 1204  are shown below planes representing the cloud-director layer of abstraction  1206 - 1208 . Above the planes representing the cloud-director level of abstraction, multi-tenant virtual data centers  1210 - 1212  are shown. The resources of these multi-tenant virtual data centers are securely partitioned in order to provide secure virtual data centers to multiple tenants, or cloud-services-accessing organizations. For example, a cloud-services-provider virtual data center  1210  is partitioned into four different tenant-associated virtual-data centers within a multi-tenant virtual data center for four different tenants  1216 - 1219 . Each multi-tenant virtual data center is managed by a cloud director comprising one or more cloud-director servers  1220 - 1222  and associated cloud-director databases  1224 - 1226 . Each cloud-director server or servers runs a cloud-director virtual appliance  1230  that includes a cloud-director management interface  1232 , a set of cloud-director services  1234 , and a virtual-data-center management-server interface  1236 . The cloud-director services include an interface and tools for provisioning multi-tenant virtual data center virtual data centers on behalf of tenants, tools and interfaces for configuring and managing tenant organizations, tools and services for organization of virtual data centers and tenant-associated virtual data centers within the multi-tenant virtual data center, services associated with template and media catalogs, and provisioning of virtualization networks from a network pool. Templates are virtual machines that each contains an OS and/or one or more virtual machines containing applications. A template may include much of the detailed contents of virtual machines and virtual appliances that are encoded within OVF packages, so that the task of configuring a virtual machine or virtual appliance is significantly simplified, requiring only deployment of one OVF package. These templates are stored in catalogs within a tenant&#39;s virtual-data center. These catalogs are used for developing and staging new virtual appliances and published catalogs are used for sharing templates in virtual appliances across organizations. Catalogs may include OS images and other information relevant to construction, distribution, and provisioning of virtual appliances. 
     Considering  FIGS.  10  and  12   , the VI management server and cloud-director layers of abstraction can be seen, as discussed above, to facilitate employment of the virtual-data-center concept within private and public clouds. However, this level of abstraction does not fully facilitate aggregation of single-tenant and multi-tenant virtual data centers into heterogeneous or homogeneous aggregations of cloud-computing facilities. 
       FIG.  13    illustrates virtual-cloud-connector nodes (“VCC nodes”) and a VCC server, components of a distributed system that provides multi-cloud aggregation and that includes a cloud-connector server and cloud-connector nodes that cooperate to provide services that are distributed across multiple clouds. VMware vCloud™ VCC servers and nodes are one example of VCC server and nodes. In  FIG.  13   , seven different cloud-computing facilities are illustrated  1302 - 1308 . Cloud-computing facility  1302  is a private multi-tenant cloud with a cloud director  1310  that interfaces to a VI management server  1312  to provide a multi-tenant private cloud comprising multiple tenant-associated virtual data centers. The remaining cloud-computing facilities  1303 - 1308  may be either public or private cloud-computing facilities and may be single-tenant virtual data centers, such as virtual data centers  1303  and  1306 , multi-tenant virtual data centers, such as multi-tenant virtual data centers  1304  and  1307 - 1308 , or any of various different kinds of third-party cloud-services facilities, such as third-party cloud-services facility  1305 . An additional component, the VCC server  1314 , acting as a controller is included in the private cloud-computing facility  1302  and interfaces to a VCC node  1316  that runs as a virtual appliance within the cloud director  1310 . A VCC server may also run as a virtual appliance within a VI management server that manages a single-tenant private cloud. The VCC server  1314  additionally interfaces, through the Internet, to VCC node virtual appliances executing within remote VI management servers, remote cloud directors, or within the third-party cloud services  1318 - 1323 . The VCC server provides a VCC server interface that can be displayed on a local or remote terminal, PC, or other computer system  1326  to allow a cloud-aggregation administrator or other user to access VCC-server-provided aggregate-cloud distributed services. In general, the cloud-computing facilities that together form a multiple-cloud-computing aggregation through distributed services provided by the VCC server and VCC nodes are geographically and operationally distinct. 
     Distributed-Search Engine 
     The current document is directed to a distributed resource-exchange system that employs a distributed-search subsystem to identify potential resource exchanges and select, from the identified potential resource exchanges, resource exchanges that best meet specified requirements and constraints. The distributed-search subsystem provides an auction-based method for matching of resource providers to resource users within a very large, distributed aggregation of virtual and physical data centers owned and managed by a large number of different organization. The distributed-search subsystem, however, is a general searching subsystem that can be used for many additional distributed-search operations. 
     Distributed searches are initiated by distributed-search participants, which may be any type of processor-controlled device that supports access to a distributed-search application programming interface (“API”) or graphical user interface (“UI”). In a described implementation, the distributed-search subsystem comprises one or more local instances and one or more distributed-search engines. In the described implementation, local instances execute as web-application plug-ins within one or more virtual machines of a management subsystem. However, many alternative implementations are possible, including standalone applications and even hardware appliances. The local instances support the distributed-search API and/or UI, store local-instance data to support the distributed-search API and/or UI, and exchange request messages and response messages with the one or more distributed-search engines to initiate distributed searches, add attributes to a set of centrally stored attributes, and manage operation of the distributed-search subsystem. The one or more distributed-search engines communicate with local instances, centrally store various types of distributed-search-subsystem data, and carry out distributed searches on behalf of requesting local instances, maintaining an active search context for each search. 
     Entities for which searches are carried out can be of many different types, from information and data to hardware components and subsystems, automated services, products, remote computer systems connected to the distributed computer system, human users of those systems, and various types of computers, information, devices, and information accessible to the remote computer systems. The entities are characterized by attribute/value pairs. For example, a computational resource might be characterized by the attribute/value pairs: memory/2 GB; processor_bandwidth/1.2 GHz; network_bandwidth/100 MB\sec. Search results may include the values for one or more attributes as well as identifying information for providers, network addresses, and additional information. 
     Searches are parameterized by attribute/value pairs. These parameters may specify a scope for the search, minimum requirements for successful responses, search termination conditions, and many other operational parameters that allow searches to accurately tailored to user and participant needs. Participants may also be characterized by attribute/value pairs. For example, participants may be characterized by ratings that reflect past performance in supplying requested products and services. 
       FIGS.  14 A-C  illustrate components and general operation of the distributed-search methods and subsystems.  FIG.  14 A  uses illustration conventions, which are next described, that are subsequently used in  FIG.  14 C . A large distributed computer system is represented, in  FIGS.  14 A and  14 C , by four sets  1402 - 1405  of computers, each set representing a virtualized-server cluster, virtual data center, or group of virtual data centers. In large distributed computer systems, there may be tens, hundreds, or more server clusters and virtual data centers linked together by many layers of internal and external communications systems. In  FIGS.  14 A and  11 C , local internal communications are represented by interconnecting lines or channels, such as local network  1406  within server cluster or virtual data center  1403 , and one or more wide-area networks or other external communications systems are represented by cloud  1407 . The distributed-computer-system representation used in  FIGS.  14 A-C  is abstracted to provide for concise and simple illustration of the currently disclosed distributed-search methods and subsystems. 
     In the example distributed computer system shown in  FIGS.  14 A and  14 C , a management subsystem is implemented as a a multi-tiered application  1408  including two or more virtual machines  1409 - 1410  within a management server  1412  of a server cluster or virtual data center  1405 . The management subsystem displays a management user interface  1414  on one or more management consoles  1416  used by system managers or administrators to manage operation of a server cluster or virtual data center. Each server cluster or virtual data center, such as server clusters or virtual data centers  1402 - 1404 , may also include a management subsystem, such as the management subsystem  1408 - 1410  within server cluster or virtual data center  1405 . In certain implementations, a management subsystem may span two or more server clusters or virtual data centers. 
     The management subsystem provides a comprehensive server cluster or virtual data center management interface to system administrators. Through the management user interface, system administrators specify operational parameters that control facilities that store, manage, and deploy multi-tiered application and VM templates, facilities that provide for high-availability virtual-machine execution, tools for migrating executing VMs among servers and execution environments, VM replication, and data backup and recovery services. 
       FIG.  14 B  illustrates one implementation of a high-level architecture of the management subsystem  1408 - 1410  discussed above with reference to  FIG.  14 A . In the management subsystem, a first virtual machine  1418  is responsible for providing the management user interface via an administrator web application  1420 , as well as compiling and processing certain types of analytical data  1422  that are stored in a local database  1424 . In addition, the first virtual machine runs numerous custom web applications  1426 - 1427  that provide additional functionalities accessible through the management user interface. The first virtual machine also provides an execution environment for a distributed-search web application  1428  that represents a local instance of the distributed-search subsystem within a server cluster, virtual data center, or some other set of computational resources within the distributed computer system. A second virtual machine  1430  is primarily concerned with collecting metrics  1432  from various types of components, subcomponents, servers, network-storage appliances, and other components of the distributed computing system via analytics messaging  1434  and then analyzing the collected metrics  1436  to provide continuous representations of the status and state of the distributed computer system, to automatically identify various types of events and problems that are addressed automatically, semi-automatically, or manually by system administrators, and to provide additional types of monitoring and analysis, the results of which are stored in several local databases  1438 - 1439 . 
     As shown in  FIG.  14 C , the local instance of the distributed-search subsystem ( 1428  in  FIG.  14 B ) is invoked, in one implementation, through the management user interface to provide a distributed-search user interface  1440  to a system administrator or, in other cases, to provide a distributed-search application programming interface (“API”) to various automated management and computational-resource-distribution subsystems within the distributed computer system. Communication between the management subsystem  1408  and the system console  1416  is provided, in one implementation, over a secure virtual management network within the distributed computer system, represented in  FIGS.  14 A and  14 C  by dashed lines, such as dashed line  1442 . The distributed-search user interface  1440  provides facilities for the creation and storage of search policies, filters, and search queries, further discussed below. The distributed-search user interface also provides various types of administration operations and functionalities. A user launches searches through the distributed-search user interface and automated subsystems launches searches through a distributed-search API, both provided by a local instance of the distributed-search subsystem. A search initiated by specifying filters, policies, and search-result evaluation criteria previously created and stored through the distributed-search user interface or distributed-search API. 
     A search is initiated by the transmission of a search-initiation request, from the distributed-search user interface or through a remote call to the distributed-search API  1444 , to a local instance of the distributed-search subsystem within the management subsystem  1408 . The local instance of the distributed-search subsystem then prepares a search-request message that is transmitted  1446  to a distributed-search engine  1448 , in one implementation implemented as a a multi-tiered application containing one or more distributed-search-engine virtual machines that runs within a server or other computer system within the distributed computer system. The distributed-search engine transmits dynamic-attribute-value requests to each of a set of target participants within the distributed computing system, as represented by arrows emanating from the distributed-search engine  1448  and directed to each of a particular component or layer within the computer systems of the distributed computer system. The transmission may occur over a period of time in which batches of dynamic-attribute-value requests are transmitted at intervals, to avoid overloading communications subsystems. The set of target participants is obtained by using filters included within the search request to evaluate centrally stored static attribute values for entities within the distributed computer system, as discussed, in detail, below. Initial filtering avoids transmission of messages to entities incapable of satisfying search-request criteria. Note that the target participants may be any type or class of distributed-computing-system component or subsystem that can support execution of functionality that receives dynamic-attribute-value-request messages from a distributed-search engine. In certain cases, the target participants are components of management subsystems, such as local instances of the distributed-search subsystem ( 1428  in  FIG.  14 B ). However, target participants may also be virtualization layers, operating systems, virtual machines, applications, or even various types of hardware components that are implemented to include an ability to receive attribute-value-request messages and respond to the received messages. Finally, the distributed-search engine  1448  receives responses from the target participants within the distributed computer system and continuously evaluates the responses to maintain a small set of best responses. In many cases, there may be significant periods of time between reception of a dynamic-attribute-value request by a target participant and sending of a response by the target participant. When termination criteria for the search are satisfied, and the search is therefore terminated, the set of best responses to the transmitted dynamic-attribute-value-request messages are first verified, by a message exchange with each target participant that furnished the response message, and are then transmitted  1452  from the distributed-search engine to one or more search-result recipients  1454  specified in the initial search request. A search-result recipient may be the local instance of the distributed-search subsystem that initiated the distributed search, but may alternatively be any other component or entity or set of components or entities of the distributed computer system that supports reception of a distributed search-results message. 
       FIGS.  15 A-C  illustrate certain of the information and data entities used within the distributed-search methods and subsystems. The distributed search is used to identify entities managed by, contained within, or accessible to distributed-search participants. These entities are characterized by attribute/value pairs. An entity may be a participant, a service, information, distributed-computer-system components, remote computers connected through communications media with the distributed computer system, remote-computer users, or any of many other types of entities that can be characterized by attribute values and that are desired to be identified through distributed searches. 
       FIG.  15 A  illustrates an attribute/value pair. The attribute  1502  is an alphanumeric string that identifies a particular attribute within a universal set of attributes used by the distributed-search methods and subsystems. Attributes are, in many implementations, centrally stored and managed by one or more distributed-search engines. An attribute is instantiated by being associated with one or more any of the above-mentioned types of entities. Instantiated attributes are associated with values. In this respect, an attribute is similar to a variable used in programming-language statements. The variable has a name, is instantiated within a particular scope comprising the routines from which it is visible, and an instantiated variable can store any of various different values within the value domain of the variable. 
     In the currently disclosed distributed-search methods and subsystems, three types of attributes are generally encountered: (1) entity attributes  1506 , which are associated with entities that are identified by searches; (2) search attributes  1507 , which identify particular parameters for a given distributed search; and (3) search-participant attributes  1508 , which characterize a participant, generally a participant initiating a distributed search. Entity attributes  1506  fall into two classes: (1) static entity attributes  1509 , which are entity attributes that, when instantiated, have either constant values or have values that are only infrequently changed and can therefore be pre-fetched and stored by the distributed-search engine in advance of being used during the initiation of distributed searches; and (2) dynamic entity attributes  1510 , which are frequently modified and are therefore retrieved, at search time, by transmitting dynamic-attribute-value-request messages to target participants. The value  1504  currently associated with an instantiated attribute  1502  in an attribute/value pair is generally represented by an alphanumeric string. Attribute values can be numeric  1512 , elements of a set  1513 , elements of an ordered set  1514 , Boolean values  1515 , or generalized calls to functions or procedures that return numeric, set, ordered-set, or Boolean values  1526 . A value may be one of a single element of a set, a subset of a set, single numeric values, or numeric-value ranges. In  FIG.  15 A , examples of the various different types of values are given in parentheses, such as the example range “[3-7.36]”  1517  provided for the mixed-range subtype  1518  of the numeric  1512  value type. 
       FIG.  15 B  shows certain derived types of information and data used by the distributed-search methods and subsystems to which the current application is directed. Values may be combined in value expressions  1520 . These are familiar arithmetic and set expressions that include binary arithmetic operators  1522  and binary set operators  1523  as well as various types of arithmetic and set unary operators  1524 . Value expressions can be considered to be expressions equivalent to constant values. Similarly, attributes may be combined in attribute expressions  1526  which are equivalent to expressions in programming languages that include variables. When the attributes in an attribute expression are replaced by specific values with which they are associated, the attribute expression is equivalent to a constant value. A derived attribute  1528  is an attribute defined in terms of other attributes. Value expressions can be combined by common relational operators to produce relational value expressions  1530  using relational binary operators  1532 , relational unary operators  1534 , and logical operators  1536 . 
       FIG.  15 C  illustrates additional data and information types used in the distributed-search methods and subsystems to which the current application is directed. A filter  1540  is a relational expression that specifies a value or range of values for an attribute. A policy  1542  comprises one or more filters. A search-evaluation expression  1544  is used to evaluate returned dynamic-attribute values from participant search-request responders in order to compute a score for a response, as discussed, in detail, below. A search-evaluation expression comprises one or more evaluators. An evaluator  1546  is either a simple evaluator or a weight/simple-evaluator pair. A simple evaluator  1548  is a minimum-positive attribute or a floor/minimum-positive-attribute pair. A minimum-positive attribute is an attribute having values selected from a numeric or ordered-set value domain that map to a set of numerically increasing values, generally beginning with the value “0.” As the value increases, the desirability or fitness of the attribute and its associated value decreases. For example, an attribute “price” may have values in the range [0, maximum_price], with lower prices more desirable than higher prices and the price value 0, otherwise referred to as “free,” being most desirable. In general, an attribute that is not a minimally positive can be easily transformed into a derived, minimum-positive-attribute. For example, the attribute “expected lifetime” can be transformed into the derived attribute “early expiration” by: early_expiration: MAXIMUM_LIFETIME−expected_lifetime. A weight is a numeric multiplier and a floor is a numeric or ordered-set value. Weights are used to adjust the relative importance of attributes in search-evaluation expression and a floor is used to set a lowest-meaningful value of an attribute to a value greater than 0, for numeric attributes, or to an ordered-set value greater than the minimum value in the ordered set. A search  1552  is either a search-evaluation expression or a search-evaluation expression and one or more policies. 
       FIGS.  16 A-B  illustrate certain types of data maintained and used within local instances of the distributed-search subsystem and within a distributed-search engine. As shown in  FIG.  16 A , a local instance of the distributed-search subsystem stores one or more filters  1602 , one or more policies  1604 , each policy comprising one or more filters, one or more evaluators  1606 , one or more search-evaluation expressions  1608 , each search-evaluation expression comprising one or more evaluators, and one or more searches  1610 , each search comprising a search-evaluation expression and zero, one, or more policies. In  FIG.  16 A , each row, such as row  1612 , within a set of information entities, such as the set of filters  1602 , represents a single information entity of the type of the entity set. The various types of information entities may be stored in relational database tables, including singly or multiply indexed relational database tables, or in any of many other different types of data-storage objects and systems. 
     Using similar illustration conventions as used in  FIG.  16 A ,  FIG.  16 B  shows the types of information entities stored within the distributed-search engine. The information-entity sets include a set of participants  1620 , a set of continuously collected static-attribute/value pairs associated with participants  1622 , a set of attributes  1624  and a set of attribute types  1626  which define the attributes that can be used in filters and profiles, a set of sets  1628  from which set values and subsets are selected for set-valued attributes, and a set of active search contexts  1630 , each active search context representing a distributed search currently being executed by the distributed-search subsystem. 
       FIG.  17    is a high-level diagram of the distributed-search engine. The distributed-search engine receives incoming messages from one or more communications subsystems in an input queue  1702  and outputs messages to an output queue  1704  from which they are extracted and transmitted by the one or more communications subsystems. There are many different types of messages received and transmitted by the distributed-search engine. Different types of messages can be thought of as being distributed from the input queue  1702  to input queues for specific message types, such as input queue  1706  for search requests. Similarly, specific types of output messages are output to specific output queues, such as output queue  1708 , from which they are input to the general output queue  1704  for transmission. Various different types of controllers or logic modules  1710 - 1714  process particular types of input messages and generate particular types of output messages. For example, controller  1710  receives search requests from distributed-search participants and outputs results corresponding to the search requests. Controller  1711  outputs information requests, such as dynamic attribute-value requests, and receives responses to those information requests. Controller  1712  receives UI information requests from local instances of the distributed-search subsystem and outputs responses to those requests. For example, a local instance of the distributed-search subsystem may request a current list of the different types of attributes that can be used to construct filters, policies, and search-evaluation expressions. Controller  1713  outputs static-attribute requests to distributed-search participants and receives response to those requests. Controller  1714  receives management commands and requests from local instances of the distributed-search subsystem and outputs responses to the received commands and requests. Ellipses  1716  indicate that a distributed-search engine may include additional types of controllers that receive and output additional specific types of messages. 
       FIG.  18    illustrates various messages and data structures used during execution of a distributed search by the distributed-search subsystem, including an active search context, a search request, a search-request response, and information requests and responses. A search-initiation-request message  1802  includes header information  1804  as well as a search-initiation request  1806  that includes a search-evaluation expression and zero, one, or more policies. A search-result message  1810  also includes a header  1812  and one or more search results  1814 . Search results identify entities and include attribute/value pairs that characterize the entities. An information request  1820  is sent by the distributed-search engine to target participants requesting current values for a set of dynamic attributes  1822  specified in the information-request message. A response to the information-request message  1824  includes the requested dynamic-attribute values  1826 . 
     An active search context  1830  is a complex data structure maintained by the distributed-search engine for each distributed search currently being executed by the distributed-search engine. In one implementation, an active search context includes an indication of the type of search  1832 , a start time for the search  1834 , an end time for the search  1836 , and a number of additional search parameters  1838 . The active search context may store the search-initiation-request message  1840  that initiated the search. The active search context may additionally include a batch size  1842 , indicating the number of information requests to be sent in each batch of transmitted information requests and an indication of the time at which the last batch of information-request messages was sent  1844 . Ellipses  1846  indicate that many additional parameters and information entities may be stored within an active search context. The active search context may also include a list of target participants  1850  to which information requests need to be directed. These may be participant addresses, expressions from which sets of participant addresses may be computed, or other types of information that can be used to generate addresses for target participants during execution of a distributed search. In addition, the active search context includes an indication of the number of evaluators in the search-evaluation expression  1856 , a set of evaluator queues  1858 , and a master queue  1860 . The evaluator queues maintain an ordered set of returned dynamic-attribute values corresponding to the dynamic attribute associated each evaluator in the search-evaluation expression. The master queue  1860  maintains dynamic-attribute values, scores, and other information for the participants with the best-evaluated responses so far received. Operation of the evaluator queues and master queue is discussed, in great detail, below. 
       FIGS.  19 A-B  illustrate operation of the evaluator queues and master queue within an active search context. In this example, a dynamic-attribute-value-request message, a type of information-request message, is transmitted to target participants to obtain current values for each of 3 attributes a, b, and c. The search-evaluation expression  1902  associated with the distributed search is: 3(10,a)+5b+c. The “+” operators indicate that a score is computed by adding values computed for each evaluator. The first evaluator, 3(10,a), has a weight equal to 3, a floor equal to 10, and is computed from the current value of attribute a. The second evaluator  5   b  has a weight of 5 and is computed from the current value of attribute b. The third evaluator is simply the value of attribute c. The search-evaluation expression is used to compute scores for each received response message, with lower scores more favorable than higher scores. Three evaluator queues  1904 - 1906  store, in sorted order, the values for attributes a, b, and c for the participant responses stored in the master queue MQ  1908 . The number of stored responses is indicated in the variable num  1909 . In  FIGS.  19 A-B , the state of the evaluator queues and the master queue are indicated before and after reception of each of a series of responses to dynamic-attribute-value-request messages. Initially, the queues are empty  1910 . After a first response  1912  is received, an entry is placed in each queue, resulting in the queue state  1914 . The first response message  1912  includes numeric values for the three attributes a, b, and c  1915 ,  1916 , and  1917 . It is also associated with an identifier, or ID  1918 . In this example, the IDs are simple monotonically increasing integers starting with “1.” 
     Next, processing of the first response message  1912  is described. The three attribute values  1915 - 1917  are entered into their respective queues  1920 - 1922 . Because the queues are initially empty, they become the first entries in the queues and are therefore in sorted order. Then, a score is computed using the search-evaluation expression  1902 . First, if a returned value is less than the floor in the evaluator associated with the attribute value, an initial evaluator score is set to the floor value. Otherwise, the initial evaluator score is set to the value returned in the response message. Then, a percentage or ratio is computed for each initial evaluator score and the maximum value in the queue in which the associated attribute value was inserted. The ratio is multiplied by 100 to generate an intermediate evaluator score in the range [0, 100]. Then, the intermediate evaluator score is multiplied by the weight to produce a final evaluator score. The three evaluator scores are then added to produce the final score for the response message. In the case of the first response message  1912 , all of the returned attribute values are the maximum values in the queues. Therefore, the score is computed as:
 
(3×((30÷30)×100))+(5×((25÷25)×100))+((75÷75)×100)=900
 
This score is entered, in association with the identifier for the response message “1,” into the master queue as the first entry  1924 . There is now one entry in the master queue and each evaluator queue, so the variable num now has the value “1”  1925 . Of course, this is merely one way to compute a score from the search-evaluation expression and returned attribute values. Many other types of score computations can be used. For example, the rank of an attribute value in an evaluator queue can be used in addition to, or in place of, the percentage of the maximum value in the queue to compute the intermediate evaluator score. The raw computed ratios of values to max values in queues can be used, rather than percentages. Exponentials and logarithms can be employed to generate non-linear scoring methods. Evaluator scores may be combined by operations other than addition. However, the currently described method has proven to provide good results for certain multi-attribute search results.
 
     A second response message  1926  is then received, and the same operations are performed. Because the values in the evaluator queues are sorted in ascending order, and because the value “100” for attribute c in the second response message  1927  is greater than the value “75” for attribute c in the first response message  1917 , the value “100” is now at the end of the evaluator queue  1928  for attribute c. The scores for the first and second messages are now recomputed as:
 
(3×((30÷30)×100))+(5×((25÷25)×100))+((75÷100)×100)=875
 
(3×((22÷30)×100))+(5×((20÷25)×100))+((100÷100)×100)=720
 
In the illustrated queue states, the master queue is kept sorted, in ascending order, so the score and identifier for the second response message occupies the first position  1929  in the master queue and the identifier and score for the second response message now occupies the second position  1930  in the master queue. Again, the lower the score, the more desirable the response. As will be seen, below, the active search context is designed to retain a set of the lowest-scored response messages, alternatively referred to as “most favorably scored response messages,” received during the course of the distributed search.
 
     A third response message  1932  is then received, and the same operations are performed. In this case, the value for attribute a, “7,”  1934  is lower than the floor “10” for the first evaluator, so the value “10” is used instead of the value “7” in computing the evaluator score associated with attribute a. The scores for all three messages are recomputed as:
 
(3×((30÷30)×100))÷(5×((25÷27)×100))÷((75÷100)×100)=837
 
(3×((22÷30)×100))÷(5×((20÷27)×100))÷((100÷100)×100)=690
 
(3×((10÷30)×100))÷(5×((27÷27)×100))÷((54÷100)×100)=654
 
In this example, the master queue is kept sorted, in ascending order, so the score and identifier for the second response message occupies the first position  1929  in the master queue and the identifier and score for the second response message now occupies the second position  1930  in the master queue.
 
     Four more response messages  1936 - 1939  are received, resulting in the queue state  1940  shown in  FIG.  19 B . At this point, the evaluator queues and the master queue are full. From now on, any newly received response message added to the master queue along with individual attribute values added to the evaluator queues, will involve discarding an entry from each queue. This only occurs when the score computed for the newly received response message is lower than one of the scores in the master queue. As more and more responses are received, the likelihood that any next received response will be entered into the evaluator and master queues quickly decreases to a relatively low value for most types of distributed searches. The operations now become slightly more complex. First, as shown in a scratch-pad representation  1942  of the evaluator and master queues, there is an additional entry in each queue that can temporarily accommodate the attribute values and score for a newly received message. The scores are computed based on all of the entries, including those for the newly arrived response, and then the entries for the response with the highest score are deleted. Newly arrived response  1944  with ID equal to “8” ends up with a score “658,” placing it towards the middle  1946  of the scratch-pad master queue. The score for response message “7”  1948  is now highest, and therefore the entries for that response message are deleted from the queues to produce queue state  1950 . 
     The ninth response message  1952  arrives with each attribute value greater than the current maximum value in the respective evaluator queue. As a result, no new scores need be computed, since there is no possibility that a score computed for the ninth response message could be lower than any of the scores currently residing in the master queue. The ninth response is thus immediately rejected and the queue state  1954  remains unchanged. 
     A Distributed Resource-Exchange System that Aggregates a Large Number of Data Centers to Create a Distributed, Multi-Organization Cloud-Computing and Resource-Sharing Facility 
       FIGS.  20 A-E  illustrate the concept of resource exchange among cloud-computing facilities, data centers, and other computing facilities.  FIGS.  20 A-D  all use similar illustration conventions, next described with reference to  FIG.  20 A . 
       FIG.  20 A  shows abstract representations of four different computing facilities  2002 - 2005 . In each large rectangle representing each computing facility, smaller squares represent a capacity for hosting a VM. Squares without cross-hatching, such as square  2006 , represent a currently unused capacity for hosting a VM and cross-hatched squares, such as square  2008 , represent a currently in-use capacity for hosting a VM. Of course, real-world computing facilities generally have the resources and capacities to host hundreds, thousands, tens of thousands, or more VMs, but, for current concept-illustration purposes, the 24-VM-hosting capacity of each illustrated computing facility  2002 - 2005  is sufficient. It should be noted that, in the current document, the computational resources used to host a VM are used as an example of a resource that can be exchanged between computing facilities. The computational resources used to host a container is another example of a resource that can be exchanged between computing facilities. Virtual machines and containers are both examples of computational-resources-consuming entities that can be hosted by computing facilities. 
     As shown in  FIG.  20 A , the computing facility DC 1   2002  has no spare or unused VM hosting capacity. Computing facilities DC 2   2003  and DC 3   2004  each have unused capacity for hosting eight additional VMs while computing facility DC 4  has unused capacity for hosting three additional VMs. Unused capacity can arise within a computing facility for many reasons. A computing facility may have been expanded to accommodate a planned project or division, but the project or division may not yet need the expanded computational resources or may have been cancelled. In many cases, computational-facility administrators may maintain additional, spare capacity to be able to instantly respond to increased demand from internal users or from remote clients of internally hosted web services and applications. In some cases, the owners and/or managers of a computational facility may have configured the computational facility for providing computational resources as a service to remote clients. The amount of unused capacity within a given computational facility may fluctuate widely and over very short time spans, in certain operational states, or may remain fairly stable, over days, weeks, or months. Currently, for computing facilities other than those specifically established to provide resources as a service, there are few methodologies and media for safely and conveniently making unused capacity available to remote systems and users. 
     The distributed resource-exchange system facilitates leasing or donating unused computational resources, such as capacity for hosting VMs, by computing facilities to remote computing facilities and users. The distributed resource-exchange system provides a type of automated computational-resource brokerage that brokers exchange of computational resources among participant computing facilities, allowing computational resources to be conveniently, securely, and rationally shared among many different computing facilities owned and managed by many different participant organizations. At a high-level perspective, the automated computational-resource brokerage is a computational-facility-aggregation optimization subsystem that allows for applying computational resources to tasks that need them across a potentially enormous number of discrete computing facilities owned and managed by many different organizations. The distributed resource-exchange system provides efficient brokerage through automation, through use of the above-discussed methods and systems for distributed search, and through use of efficient services provided by virtualization layers with computing facilities, including virtual management networks, secure virtual internal data centers, and secure VM migration services provided by virtualization layers. The automated computational-resource brokerage is convenient and easy to use for administrators, managers, and other users of commutating facilities seeking to sell, donate, or otherwise provide local resources to remote computing-facility resource consumers because of simplified user interfaces, because of predefined attributes, filters, profiles, and easily accessible information about resource providers and resource consumers, and because of a wealth of automated methodologies that streamline searches for resources, transactions that provide resources for defined periods of time to resource consumers, collection of user feedback, and generation of rankings, ratings, and recommendations to facilitate future searchers for resources and resource-acquisition transactions. The automated computational-resource brokerage is rational because the brokerage provides a wealth of information to resource providers and resource consumers in order that participants are fully informed with regard to available resources and their attributes, and because this information is incorporated into automated methods and systems that allow the wealth of information to be constantly updated and to be used by automated distributed-search methods. The automated computational-resource brokerage provides secure remote hosting of VMs, secure data transmission and storage, secure internal and external network communications, and other security measures to ensure that resources provided by remote computing facilities are as secure, or nearly as secure, as local resources used by resource consumers. 
       FIG.  20 B  illustrates an initial step in resource exchange. Computing facilities DC 2   2003  and DC 3   2004  have registered as participants with the automated computational-resource brokerage in order to make their spare VM-hosting capacity available to remote resource consumers. As shown in  FIG.  20 B , they have provided attribute values  2010  and  2012  to the automated computational-resource brokerage indicating that they are interested in selling VM-hosting capacity. As discussed above, certain of these attribute values are provided during registration, others are provided in response to static-attribute requests, and still others are provided in response to information-request messages. Attributes such as the current price for VM hosting and current hosting capacity are likely to be provided in response to information-request messages, while the types of hosting services and long-term hosting capacities may be provided in response to static-attribute requests. The fact that computing facilities DC 2  and DC 3  are automated-computational-resource-brokerage participants is obtained during registration with the automated brokerage. 
     In  FIG.  20 C , the administrator of computing facility DC 1   2003  realizes that all hosting capacity is currently in use within the computing facility. As a result, the administrator can either seek to physically expand the computing facility with new servers and other components or seek to obtain computational resources for remote providers, both for launching new VMs as well as for offloading currently executing VMs. As shown in  FIG.  20 C , the administrator has elected to register as a participant with the automated computational-resource brokerage and has initiated a search for one or more remote provider-participants to host five VMs  2014 . 
     In  FIG.  20 D , the administrator of computing facility DC 1   2002  has received search results  2016  from the automated computational-resource brokerage. The administrator, or automated resource-acquisition functionality within a local client instance of the automated computational-resource brokerage, can choose with which provider to transact for VM hosting, or can transact with both providers for hosting a different subset of the five VMs. Note that, during the time that the search was initiated, as discussed above with reference to  FIG.  20 C , and when initial information may have been returned from computing facility DC 2  to computing facility DC 1 , several new VMs have been hosted by computing facility DC 2 . However, because the distributed search verifies respondents prior to returning search results, as discussed above, the search results  2016  accurately reflect the current hosting capacity of computing facility DC 2 . 
     In  FIG.  20 E , the administrator of computing facility DC 1 , or automated resource-acquisition functionality within a local client instance of the automated computational-resource brokerage, has decided to transact for hosting the five VMs with computing facility DC 2 . As shown by the dashed lines  2016  that demarcate the 5 DC 1  VMs  2018 - 2022  hosted by computing facility DC 2 , the VMs are hosted in a secure hosting partition so that neither the executing VMs nor the internal resources that they use within computing facility DC 2  can be accessed or observed by DC 2  entities or users. These 5 hosted VMs can be thought of as running within an extension of the DC 1  computing facility. 
       FIGS.  21 A-B  illustrate implementation of the automated computational-resource brokerage within multiple distributed computing facilities. The implementation of the computational-resource brokerage mirrors implementation of the distributed-search subsystem discussed above with reference to  FIGS.  11 B-C . The management subsystem is again shown, in  FIG.  21 A , using the same numeric labels used previously in  FIG.  11 B . In addition to the distributed-search web application  1128  that represents a local instance of the distributed-search subsystem within a server cluster, virtual data center, or some other set of computational resources within the distributed computer system, the management system provides an execution environment for a cloud-exchange web application  2102  that represents a local instance of the automated computational-resource brokerage within the server cluster. In certain implementations, the distributed-search web application  1128  may be incorporated within the cloud-exchange web application. The cloud-exchange web application  2102  provides a cloud-exchange UI ( 2104  in  FIG.  21 B ) through which users can register as participants, update participant information, develop exchange policies and filters, set up automated resource-provision and resource-consumption agents within the automated computational-resource brokerage, and monitor exchanges, transactions, and other activities. 
     As shown in  FIG.  21 B , the local instance of the automated computational-resource brokerage, or cloud-exchange web application ( 2102  in  FIG.  21 A ) exchanges requests and responses with a cloud-exchange engine  2105 , in one implementation implemented as a a multi-tiered application containing multiple cloud-exchange engine virtual machines  2106 - 2109  that run within a server  2110  or other computer system within the distributed computer system. The cloud-exchange engine maintains centralized attribute values and other data for the automated computational-resource brokerage, monitors transactions, carries out transactions for computational resources on behalf of participants, collects feedback and maintains ratings and/or rankings of participants, provides many default filters and policies, and carries out many additional functions that together comprise the automated computational-resource brokerage. 
       FIG.  22    illustrates the general implementation of the cloud-exchange engine ( 2105  in  FIG.  21 B ). The general implementation of the cloud-exchange engine  2202  mirrors that of the distributed-search engine  2204 , discussed above with reference to  FIG.  14   . Incoming request and response messages are received in a general input queue  2206  and outgoing responses and requests are queued to a general output queue  2208 .  FIG.  14    is a high-level diagram of the distributed-search engine. There are many different types of messages received and transmitted by the cloud-exchange engine. Different types of messages can be thought of as being distributed from the input queue  2206  to input queues for specific message types, such as input queues  2210 - 2212 . Similarly, specific types of output messages are output to specific output queues, such as output queue  2214 - 2216 , from which they are input to the general output queue  2208  for transmission. Various different types of controllers or logic modules  2218 - 2220  process particular types of input messages and generate particular types of output messages. For example, controller  2218  receives registration requests and additional requests within registration dialogues and returns responses to those requests. Searches for resources, also considered to be requests for resource consumption or initiation of resource auctions, are processed by a search-pre-processing module  2222  before being input as search requests to the distributed-search engine. Search responses, or bids from resource-provider participants, are processed by a search-post-processing module  2224  before being returned to the resource-consumption participant that initiated the search or auction. Of course, many alternative implementations, including implementations that incorporate distributed-search logic directly within the cloud-exchange engine, are possible. 
     Resource-Exchange Life Cycle as Represented by a Resource-Exchange Context 
     In many implementations of the above-described resource-exchange system, each resource exchange involves a well-defined set of operations, or process, the current state of which is encoded in a resource-exchange context that is stored in memory by the resource-exchange system to facilitate execution of the operations and tracking and monitoring of the resource-exchange process. The well-defined set of operations, and the state changes associated with those operations, define the life cycle of a resource exchange within the resource-exchange system. Resource-exchange contexts are physical components of the resource-exchange system. Resource-exchange contexts persistently store policy information and state information that can be electronically accessed during resource-exchange-system operations. Resource-exchange contexts are also control components of resource-exchange system, organizing and driving the many different tasks carried out by many different resource-exchange-system components within many different computing facilities. 
     To facilitate understanding of the following discussion, terminology used to describe the resource-exchange system and resource-exchange-system components is next presented. The phrase “resource-exchange system” refers to a large number of computing facilities owned and managed by many different organizations that are partially aggregated to allow the computing facilities to share portions of their computational resources with other computing facilities. The phrase “resource-exchange context” refers to the information stored in memories and mass-storage devices of the resource-exchange system that encodes an indication of the current state of a particular resource exchange, a buy policy associated with the resource exchange, an active search context during at least an auction phase of the lifecycle of the resource exchange, and additional information. The phrase “resource exchange” is an exchange of a computational resource, provided for a specified time period by a resource-provider computing facility, for a fee, service, or computational resource provided by a resource-consumer computing facility. The cloud-exchange system is an automated computational-resource brokerage system, as discussed in the preceding section. The resource provider and the resource consumer, both computing-facility participants in a resource exchange, each includes a local cloud-exchange instance which provides a cloud-exchange UI and which carries out client-side tasks in support of a resource exchange that is managed by the cloud-exchange system. 
       FIGS.  23 A-G  illustrate, for one implementation of the resource-exchange system, the process by which resource needs are communicated from resource consumers to resource providers, resource providers offer resources to resource consumers, one or more resource providers are selected for provision of particular resources via a distributed-search-engine-mediated resource-provision auction, and resources are allocated on behalf of resource consumers by the one or more selected resource providers. In the implementation described below, the cloud-exchange system receives requests, from resource consumers, to place virtual machines into computing facilities of resource consumers for execution. Of course, this represents only one of many possible resource exchanges. The cloud-exchange system identifies candidate computing facilities for placement of the virtual machines using a distributed-search-based auction, receives bids for executing the virtual machines by candidate computing facilities, and selects computing facilities for placement of the virtual machines based on the received bids. The cloud-exchange system uses a third-party transaction system for carrying out financial transactions associated with placement and execution of virtual machines by resource-provider computing facilities on behalf of resource-consuming computing facilities. The process by which virtual machines are placed for execution is complex, policy driven, and generally fully automated, although semi-automated virtual-machine placement is also supported by the cloud-exchange system. Because of the complexity and information-driven characteristics of virtual-machine placement by the cloud-exchange system, resource-exchange contexts are necessary for organizing, initiating, and tracking the complex informational, resource, and financial transactions involved in virtual-machine placement. 
       FIG.  23 A  illustrates, in block-diagram form, a resource consumer, the cloud-exchange system, and a resource provider that are involved in resource exchange that comprises virtual-machine placement and execution along with various types of information maintained within the resource consumer, the cloud-exchange system, and the resource provider to facilitate virtual-machine placement. The resource consumer  2302 , a computing facility that contracts with one or more resource-provider computing facilities for execution of one or more virtual machines, includes a local cloud-exchange instance. The cloud-exchange system  2304  is an automated computational-exchange brokerage system that carries out matching of resource consumers to resource providers, using the distributed-search methods discussed above in previous subsections, placement of virtual machines, and initiating financial transactions at the completion of virtual-machine execution. The resource provider  2306 , a computing facility that hosts virtual machines for resource consumers, includes a local cloud-exchange instance. A computing facility may be both a resource consumer and a resource provider. Such computing facilities may offload their own virtual machines of relatively little importance and priority to other computing facilities through the resource-exchange system in order to host more important and higher priority virtual machines on behalf of other computing facilities. The cloud-exchange system may act as a complex, near-real time load balancing and load optimization engine for the computing facilities that are both resource providers and resource consumers. 
     A resource consumer  2302  maintains descriptions, and may maintain executables, for currently non-executing virtual machines  2306 - 2307  and generally hosts a number of currently executing virtual machines  2308 . In addition, for each described virtual machine or executing virtual machine, the resource consumer generally maintains information about the operational characteristics of the virtual machine  2309 , referred to as a “VM personality.” A resource consumer maintains various attribute-value pairs that describe the resource consumer, including dynamic attribute-value pairs  2310  and static attribute-value pairs  2311 . The dynamic and static natures of the attribute-value pairs are those described above, in a previous subsection, in which the distributed-search method used by the cloud-exchange system is described. The resource consumer also maintains one or more by policies, such as buy policy  2312 . A buy policy is a policy comprising a set of filters, as discussed in the preceding subsection describing the distributed-search method. A resource provider  2306  contains one or more sell policies, such as sell policy  2314 , dynamic  2316  and static  2318  attribute-value pairs describing the resource provider and hosting services offered by the resource provider, pricing information  2320  used for calculating hosting bids, and a great deal of additional information  2322  regarding current load and capacity for hosting virtual machines on behalf of resource consumers. In addition, a resource provider may maintain data related to the performance characteristics of various types of virtual machines hosted by the resource provider  2324 . 
     The cloud-exchange system  2304  includes various types of data associated with cloud-exchange participants, such as the data  2326  maintained for a participant P 0 . This data may include a copy of static attributes  2328 , operational and performance characteristics of virtual machines associated with the participant  2329 , additional data describing the participant and the participants virtual-machine-hosting and virtual-machine-outsourcing activities  2330 , and additional context information associated with the participant  2331 . The virtual-machine placement process employs an auction carried out by the above-described distributed-search engine. The auction is essentially a distributed search for resource providers that can best host one or more virtual machines on behalf of a resource consumer. During the auction, and during virtual-machine hosting, the cloud-exchange system maintains an active search context  2332  for each resource exchange involving placement and execution of one or more virtual machines. The active search context is a significant portion of the resource-exchange context maintained by the cloud-exchange system. However, the resource-exchange context additionally includes information stored in a resource consumer, information stored in a resource provider, and information associated with both the resource consumer and resource provider stored within the cloud-exchange system. 
       FIG.  23 B  illustrates certain initial process steps of the virtual-machine placement and execution process carried out within a resource consumer. In  FIG.  23 B , and in subsequently described  FIGS.  23 C-G , a resource consumer, resource provider, and the cloud-exchange system are represented by large rectangles, such as large rectangle  2336  representing a resource consumer in  FIG.  23    B. Two such rectangles with a small arrow in between, such as rectangles  2336 - 2337  in  FIG.  23 B  and small arrow  2338 , represent a change of state within a single resource consumer, resource provider, or the cloud-exchange system represented by the two rectangles. Larger arrows, such as arrow  2339  in  FIG.  23 C , portions of which are contained within large rectangles, represent transmission of data among the Client-Exchange system, resource consumer, and resource provider. 
     In an initial step shown in  FIG.  23 B , a systems administrator or another member of an organization managing a resource-consumer computing facility manually associates a buy policy  2340 , through the UI provided by the local cloud-exchange instance, with a set of one or more virtual machines  2341 . The association of a buy policy with a set of one or more virtual machines renders the one or more virtual machines potential candidates for outsourcing to one or more resource providers. The buy policy includes various filters that specify a resource-outsourcing policy for one or more virtual machines. The policy may include specifications of desired hosting parameters and parameter ranges, such as a maximum desired migration time, minimum and maximum network latency, resource-provider support policies, resource-provider characteristics, including reputation, resource-consumer and resource-provider eviction policies, the amount of various types of resources needed for virtual-machine execution, a scoring function for scoring bids for virtual-machine hosting submitted by resource providers through the auction process, and other such policy components. The buy policy essentially defines the computational resources desired by the resource consumer and the constraints and parameters that define resource-consumer-desired selection of one or more resource providers for hosting the resource consumer&#39;s virtual machines. 
     In a next step, a buy-policy-associated set of one or more virtual machines  2342  is activated  2343 . Activation may be carried out manually, by interaction of a system administrator or other employee of the organization managing the resource-consumer computing facility with a UI provided by the local cloud-exchange instance. Alternatively, activation may occur automatically in response to an alert or trigger generated within the local cloud-exchange instance. As an example, the local cloud-exchange instance may be configured to generate an alert when resource capacities within the resource consumer fall below minimum threshold values or rise above other threshold values. As another example, the local cloud-exchange instance may be configured to generate triggers at specified future times or when particular conditions arise within the resource-exchange system. Once a buy-policy-associated set of one or more virtual machines has been activated, the local cloud-exchange instance within the resource consumer generates an initiation-request message  2344  and transmits the initiation-request message to the cloud-exchange system. The initiation-request message  2344  contains descriptions of the one or more VMs that the resource consumer seeks to outsource, the buy policy associated with the one or more virtual machines, and may additionally include VM personality information and selected dynamic attributes describing the resource consumer. Of course, rather than a single initiation-request message, the resource consumer and cloud-exchange system may exchange multiple messages, in certain implementations, to initiate a resource exchange. 
       FIG.  23 C  illustrates initial cloud-exchange-system process steps in the virtual-machine placement and execution process. The initiation-request message  2344 , generated and transmitted by the resource consumer, is received by the cloud-exchange system  2304 . In response to receiving the initiation-request message, the cloud-exchange system uses information contained in the initiation-request message as well as additional information contained in resource-consumer-associated information stored by the cloud-exchange system  2345  to prepare an active search context  2346  to represent the virtual-machine placement and execution task. As mentioned above, the active search context  2346 , discussed in a previous subsection describing the distributed-search method used by the cloud-exchange system, is a significant portion of the resource-exchange context that maintains state information for the resource exchange. In certain cases, the active search context  2346  is temporarily queued to a wait queue  2347  until, at a time specified in the buy policy associated with the set of one or more virtual machines to be outsourced, an alert or trigger  2348  results in initiation of an auction of the outsourcing request. In other cases, the cloud-exchange system immediately proceeds to a next step in which an auction of the outsourcing request is initiated  2349 . 
     Initiation of the auction involves numerous tasks and operations. The master queue and evaluator queues  2350 , described above, are allocated and/or initiated, and the active search context is prepared for the auction process. The active search context may be inserted, for example, into a list or other data structure containing active search contexts represented outsourcing requests currently under bid. The cloud-exchange system uses information in the buy policy contained in the initiation-request message as well as static resource-provider information maintained in the cloud-exchange system to filter resource providers participating in the resource-exchange system in order to prepare an initial set of candidate resource providers for the resource exchange. In addition, the cloud-exchange system may transmit information requests, including information request  2351 , to the candidate resource providers and receive and process information-request responses, such as information-request response  2352 , in order to further filter the initial set of candidate resource providers based on dynamic attributes furnished by the candidate resource providers in the information-request responses. Much of the resource-provider information is extracted from one or more seller policies maintained by resource providers. Like buy policies, seller policies are composed of one or more filters that specify various attribute values or ranges of attribute values, as discussed above in the subsection describing the distributed-search method and system. Seller policies, for example, may specify minimum and maximum lease periods, pricing information for various types of computational resources, exclusion sets based on the areas of commerce in which potential resource consumers are involved, and many other parameters and characteristics particular to the resource provider. In a first implementation, the cloud-exchange system prepares a final list of candidate resource providers, sends bid requests to each of the candidate resource providers, such as bid request  2353 , receives bid responses, such as bid response  2354 , from the candidate resource providers, and processes the bid responses using the master queue and evaluator queues  2350 , as described in the previous subsection that describes the distributed-search method and engine used by the cloud-exchange system. In a second implementation, rather than sending bid requests to remote resource providers, the cloud-exchange system automatically generates candidate-resource-provider bids on behalf of the candidate resource providers, based on information maintained within the cloud-exchange system that was previously supplied by candidate resource providers. In essence, the two implementations represent either a direct, real-time search for remote resource providers that involves communications with remote candidate resource providers or a search for resource providers based on information already obtained from remote resource providers. The second implementation, which searches for resource providers based on already obtained information, may often provide greater search efficiency and faster response times. Both implementations logically employ the previously discussed distributed-search method and engine. 
       FIG.  23 D  illustrates processing of a bid request by a resource provider according to the first implementation, discussed above, in which a direct, real-time search for remote resource providers is carried out. Information in the bid request  2356  may be extracted and stored  2357  by the resource provider. Information in the bid request  2356  is used to prepare a bid-request response  2358  that includes a calculated bid for hosting the set of one or more virtual machines represented by the bid request based on a variety of different types of information maintained within the resource provider. These activities are carried out, of course, by a local cloud-exchange instance within the resource consumer. The bid-request response  2358  is finally returned to the cloud-exchange system. In the second implementation, the bid response is prepared by the cloud-exchange engine on behalf of the resource provider. 
       FIG.  23 E  illustrates processing steps following the auction-based selection of one or more final candidate resource providers. As discussed above, during the auction, bids are maintained within the master queue and evaluator queues based on computed scores for the bids, with the scores computed from a weighted set of score-evaluation terms. When all of the candidate resource providers have responded to bid requests or when bids have been automatically generated by the cloud-exchange system on behalf of all candidate resource providers, the results from the auction are processed to determine whether or not the auction has successfully concluded. This determination is made based on buy-policy information and other parameters and considerations. When the auction has not successfully completed, the cloud-exchange system may reissue information requests to candidate resource providers in order to update resource-provider information and prepare a modified set of candidate resource providers. The cloud-exchange system may then reissue bid requests, according to the first implementation discussed above, or may automatically generate bids on behalf of the candidate resource providers, according to the second implementation, discussed above, and, in either case, continue to seek qualified resource providers. Once the action has successfully concluded, the resource providers with bids in the master queue are considered to be a set of final resource-provider candidates. The Client-Exchange system verifies the resource provider which has submitted the highest-scored bid by sending out one or more information-request messages  2360  and receiving information-request responses  2362  that are processed to determine whether the resource provider is capable of hosting the one or more virtual machines on behalf of the resource consumer according to the winning bid and other policy-based considerations. When the resource provider which submitted the highest-scored bid cannot be verified, the cloud-exchange system attempts to verify any additional final candidate resource providers in descending score order until either a final verified resource provider is obtained or the set of bids in the master queue is exhausted. When a verified resource provider is identified by the cloud-exchange system, the Client-Exchange system carries out message-based dialogues  2364 - 2371  in order to coordinate placement of the one or more resource-consumer virtual machines within the resource-provider computing facility for execution. Once the one or more virtual machines have been successfully placed and launched, the active search context  2346  is placed into an under-execution queue  2373  or otherwise marked, by the cloud-exchange System, to indicate that the active search context is no longer actively participating in an auction. 
       FIG.  23 F  illustrates virtual-machine-placement-and-execution process steps following successful completion of an auction. The resource consumer  2302  and resource provider  2306 , in cooperation with the cloud-exchange system, transfer either a description of a VM to the resource provider  2376  or carry out one of various different types of executing-VM migration operations  2377  to place the one or more VMs and launch execution of the one or more VMs within the resource provider. In the first case, the information transferred to the resource provider is sufficient for the resource provider to build a VM executable and launch execution of the VM executable within the resource-provider computing facility. In certain cases, this involves transferring a custom VM previously constructed by the resource consumer to the resource provider. As discussed above, a VM may be encapsulated within an OVF package in order to transfer the VM from one computing system to another. Alternatively, the resource consumer may have specified a desire for the resource provider to run, on behalf of the resource consumer, one or more stock VMs already resident within the resource provider. For example, the resource consumer may wish to run a particular application program that is already incorporated within a VM template by the resource provider. In the second case, there are a variety of different types of executing-VM migration technologies. Cold migration involves halting or stalling execution of the VM executing within the resource-consumer computing facility, then transferring the VM to the resource provider, and then restarting the VM. Hot migration allows an executing VM to migrate from one computing facility to another without being halted. Other types of migration involve migrating a portion of an executing VM to the resource provider while other portions of the executing VM, such as certain stored data, continue to reside within the resource-consumer computing facility. 
     The one or more virtual machines execute within the resource-a provider computing facility for some specified period of time, such as for the duration of a well-defined lease. The Clown-Exchange system provides various mechanisms by which leases can be prematurely terminated by one of the cloud-exchange system, the resource consumer, or the resource provider. The ramifications of premature termination differ depending on the source of the termination request. In some cases, a prematurely terminated virtual machine may automatically be requeued for a subsequent auction. 
     As shown in  FIG.  23 G , whether as a result of a trigger or an alert  2380  indicating expiration of a lease or as a result of a premature termination of a lease, referred to as an “eviction,” the active search context  2346  is removed from the under-execution queue  2373 , or otherwise reactivated, and the cloud-exchange system communicates with both the resource provider  2382 - 2383  and the resource consumer  2384 - 2385  to coordinate post-execution tasks involved in completing the resource exchange. This may involve, for example, migration of one or more virtual machines back to the resource consumer or to another resource provider. In addition, the cloud-exchange system provides information  2386  to a third-party financial-transaction system that handles the financial transaction by which the resource provider is compensated for hosting the resource consumer&#39;s one or more virtual machines. 
     The various process steps described above with reference to  FIGS.  23 A-G  are intended to illustrate and describe common process steps at an overview level. There are, of course, many possible special cases that arise during a resource exchange that may involve additional process steps and additional complexities. While a general indication of the types of information transfers that occur during a resource exchange is provided in the above discussion with reference to  FIGS.  23 A-G , any particular cloud-exchange implementation may employ additional or different information transfers to facilitate resource exchanges. 
     It is important to note that the resource-exchange context, which includes an indication of the current state of a resource exchange as well as many different types of information that characterize and facilitate the resource exchange, is a fundamental organizational component of the resource-exchange system. A resource-exchange context is implemented as digitally-encoded information stored in physical data-storage devices, such as electronic memories and mass-storage devices. As such, the resource-exchange context is a physical component of the resource-exchange system that is, in effect, distributed across resource consumers, resource providers, and the cloud-exchange System. The resource-exchange context, including the sequence of state changes encoded within the resource-exchange context, defines operation of the resource-exchange system with respect to each resource exchange. The resource-exchange process can be generally subdivided into three distinct phases: (1) a pre-auction phase; (2) an auction phase; and (3) a post-auction phase. The pre-auction phase includes association of buy policies with sets of virtual machines, virtual-machine activation, and generation and sending of an initiation-request message from a resource consumer to the cloud-exchange system. The auction phase includes generation of an active search context, generating a set of initial candidate resource providers, requesting bids from the candidate resource providers, scoring and queuing returned bids, selecting final candidate resource providers, and verifying a selected resource provider by the cloud-exchange system. The post-auction phase includes migrating the one or more virtual machines to the computing facility for the selected resource provider or building the one or more virtual machines within the computing facility and monitoring virtual-machine execution in order to detect and handle virtual-machine-execution termination, including initiating a financial transaction for compensating the resource provider for hosting one or more virtual machines. 
       FIGS.  24 A-C  show the states associated with a resource exchange, and the transitions between the states, that define the VM placement and execution process for the described implementation of the cloud-exchange System and that define the lifecycle of a resource-exchange context and the particular resource exchange represented by the resource-exchange context. In  FIGS.  24 A-C , states are represented by labeled circles and state transitions are represented by curved arrows. A resource context, as discussed above, includes various types of stored information within the local cloud-exchange instances of resource consumers and resource providers as well as stored information within the cloud-exchange system. For much of the lifecycle of a resource exchange, an active search context stored within the cloud-exchange system is a significant component of the resource-exchange context. During all phases of the life cycle of the resource exchange, the current state of the resource exchange is continuously maintained within the resource-exchange context. The current state defines the remaining sequence of tasks that need to be completed by each of the participants in the resource exchange in order to successfully complete the resource exchange. 
       FIG.  24    a provides a resource-consumer-centric state-transition diagram for a particular resource exchange. The resource-exchange system is considered to be in an initial state  2402  preceding the resource exchange. In the initial state, many other resource exchanges may be in progress within the resource-exchange system. However, the currently discussed state-transition diagrams are intended to illustrate the lifecycle for a particular resource exchange independently from the many other resource exchanges and other events that may be concurrently and simultaneously occurring within the resource-exchange system. For simplicity of illustration, it is assumed that a particular resource exchange involves one or more virtual machines that execute together within a particular host. It is also possible for the virtual machines of a set of one or more virtual machines to be placed into two or more different hosts. However, in this case, each of the placements can be considered to be a separate resource exchange, with the process for each separate resource exchange generally described by the state-transition diagrams provided in  FIGS.  24 A-C . 
     The resource-exchange state transitions from the initial state to a buy-policy-assigned state  2403  as a result of manual assignment, by a system administrator or other employee of the organization managing a resource-consumer computing facility, of a buy-policy to one or more virtual machines. In certain implementations, this is carried out through a local cloud-exchange user interface. In one implementation, the virtual machines may be represented by icons that can be grouped together into folders or aggregations. Buy policies may be similarly represented by icons that can be dragged and dropped onto the folders or aggregations by mouse operations directed to the local user interface. The same user interface also allows a buy policy associated with a set of one or more virtual machines to be unassigned, resulting in transition from the buy-policy-assigned state  2403  back to the initial state  2402 . These transitions are represented by curved arrows  2404 - 2405 . In the following discussion, particular transitions between states are not numerically labeled, since the curved arrows representing transitions are annotated. 
     In the buy-policy-assigned state, a set of one or more virtual machines can be thought of as a potential resource exchange. An activation event promotes such potential resource exchanges to candidate-resource-exchange status, represented by the activated state  2406 . Activation events generally fall into two broad categories of manual activation and automated activation. Manual activation involves interaction of a user with the UI provided by the local cloud-exchange instance within the resource-consumer computing facility. Automated activation can occur due to alerts and triggers, electronic events that arise when certain additional events occur or when specified conditions arise within the resource-exchange system. The local cloud-exchange instance may be configured to generate, according to the buy-policy, alerts and/or triggers at specific points in time or when various different types of conditions obtain. As one example, an alert may be triggered when the available capacity for data storage or task execution within the computing facility falls below threshold levels. There are, of course, many different possible conditions or specifications that lead to automated triggers and alerts which, in turn, lead to activation of a buy-policy-assigned set of one or more virtual machines. Once a set of one or more virtual machines is activated, the local cloud-exchange instance prepares an initiation-request message for transmission to the cloud-exchange system, which is accompanied by a transition of the resource-exchange state to the initiation-request-message-prepared state  2407 . The local cloud-exchange instance then sends the initiation-request message to the cloud-exchange system. When the initiation-request message is successfully sent, the state of the resource exchange transitions to the placement-requested state  2408 . A failure to transmit the message returns the resource-exchange state to the initiation-request-message-prepared state, in which additional attempts to send the initiation-request message may be undertaken. After a sufficient number of failures, the resource-exchange state transitions back to the buy-policy-assigned state  2403 , often with various types of error logging and error reporting to the local user interface. In alternative implementations, repeated send failures may result in a transition of the resource-exchange state back to the activated state  2406 . 
     The next states in  FIG.  24 A , described below, are again shown in  FIG.  24 B . The transitions between these states involve process steps carried out primarily by the cloud-exchange system and a resource-provider system selected to host the set of one or more VMs. Nonetheless, the local cloud-exchange instance within the resource-consumer computing facility is aware of these state transitions, in many implementations. 
     The resource-exchange state transitions from the placement-requested state  2408  to the placed state  2409  once the Client-Exchange system places the one or more virtual machines with a selected host computing facility, or resource provider. Once the set of one or more virtual machines has been placed, a successful transfer of build instructions or a successful migration of the one or more virtual machines from the resource-consumer computing facility to the host results in a transition of the resource-exchange state to the transferred state  2410 . However, a failure to transfer the build data or to migrate the set of one or more virtual machines results in a transition of the resource-exchange state to the buy-policy-assigned state  2403 , in one implementation. In alternative implementations, transitions to other states are possible when, for example, the cloud-exchange system is able to recover from such transfer failures by placing the one or more virtual machines with another host. From the transferred state  2410 , the resource-exchange state transitions to the running state  2411  when the one or more virtual machines are successfully configured and launched within the host system. Of course, during a hot migration, the configuration and launching step is merged with the migration step. Execution failure of the one or more virtual machines returns the resource-exchange state to the transferred state  2410 . A successful launch of execution or re-start of execution of the one or more VMs returns the resource-exchange state to the running state  2411 . Multiple execution failures may result in a transition from the transferred state to the terminated state  2412 . In the running state  2411 , the one or more virtual machines continue to execute until expiration of the current lease, the occurrence of a resource-consumer eviction, a host eviction, or a cloud-exchange eviction, or the occurrence of other types of execution-termination events. When the original placement request has not yet been satisfied, the resource-exchange state transitions from the terminated state back to the placement-requested state  2408  from which the Client-Exchange system can again place of the one or more virtual machines with a host for continued execution. When the initial placement request is satisfied, the resource-exchange state transitions back to the buy-policy-assigned state  2403 . 
       FIG.  24 B  provides a Client-Exchange-system-centric resource-exchange state-transition diagram. This state-transition diagram includes three states already shown in  FIG.  24 A  and discussed above. These three states are shown in with dashed circles rather than solid circles. When execution of the one or more virtual machines terminates, and the resource exchange is therefore currently in the terminated state  2412 , the resource-exchange state briefly transitions to the charge-for-VM-execution-calculated state  2414  when the cloud-exchange system collects the information for the terminated execution of the one or more virtual machines and computes a charge for the terminated execution. The resource-exchange state transitions back to the terminated state  2412  once the cloud-exchange system sends the fee information and calculated fee to a third-party transaction service. The third-party transaction service carries out the financial transactions needed for transfer of the calculated fee from the resource consumer to the resource provider. There are many different types and modes for these transaction services. The calculated fees may be automatically withdrawn from deposit accounts, in certain cases, or the third-party transaction service may forward electronic or paper bills to the organization that manages the resource-and consumer computing facility. When an initiation-request message has been received by the Client-Exchange system, and the resource-exchange state is in the placement-requested state  2408 , the resource-exchange state transitions to the placement-request-received state  2415 . When initiation of an auction is delayed, according to the buy-policy associated with the set of one or more virtual machines or because of bandwidth limitations within the Client-Exchange system, the resource-exchange state transitions to the placement-request-queued state  2416 . Otherwise, the resource-exchange state transitions to the active-context-initialized state  2417  when the cloud-exchange system uses the information transferred in the initiation-request message, along with information stored within the cloud-exchange system, to prepare an active search context for the placement request. The occurrence of a trigger or alert results in a transition from the placement-request-queued state  2416  to the active-search-context-initialized state  2417 . The resource-exchange state transitions from the active-search-context-initialized state  2417  to the candidate-sellers-determined state  2418  when the Client-Exchange system applies buy-policy filters and other information to select an initial candidate set of resource providers. In certain cases, additional information may be solicited by the cloud-exchange system from resource providers to facilitate selection of the initial candidate resource-providers set. Once an initial set of candidate resource providers has been determined, the resource-exchange state transitions to the bids-solicited state  2419  following transmission, by the Client-Exchange system, of bid solicitations to each of the initial candidate resource providers. When, after a reasonable period of time, one or more of the candidate resource providers has not responded to the bid solicitation, the resource-exchange state may transition back to the candidate-sellers-determined state  2418  in order for additional bid solicitations to be sent out by the Client-Exchange system to non-responding candidate resource providers. In the bids-solicited state  2419 , the Client-Exchange system transitions to the quote-generated-and-queued state  2420  upon receiving and processing each bid before returning to the bids-solicited state  2419  to await further bids, when bids have not been received from all candidate resource providers. When the final bid has been received, and a quote generated and queued for the bid, and when bid-closure conditions have been met, the resource-exchange state transitions to the successful-bid-closure state  2421 . When, however, one of various different types of termination conditions have instead arisen, the resource-exchange state transitions to the placement-failure state  2422 . Otherwise, the resource-exchange state may transition back to the candidate-sellers-determined state  2418  for an immediate or a delayed subsequent round of bid solicitations. When no final candidate resource providers have been obtained following a maximum number of bid-solicitation attempts, or when one of many different types of termination conditions obtain, the resource-exchange state transitions from the candidate-sellers-determined state  2418  to the placement-failure state  2422 . When a bid-closure condition obtains while the resource-exchange state is the candidate-sellers-determined state  2418 , the resource-exchange state transitions to the successful-bid-closure state  2421 . In the second, often more efficient implementation discussed above, the bids are generated by the cloud-exchange engine automatically, on behalf of the candidate resource providers, in which case the bids-solicited state  2319  and the quote-generated-and-queued state  2320  are merged with the candidate-sellers-determined state  2318 . In this second implementation, the cloud-exchange engine automatically bids on behalf of the identified candidate sellers and transitions to successful-bid-closure state  2321  or placement-failure state  2322 . When the Client-Exchange system is able to successively verify one of the final candidate resource providers, the resource-exchange state transitions to the verified-seller-found state  2423 . Otherwise, a transition to the placement-failure state  2422  occurs. From the verified-seller-found state  2423 , the resource-exchange state transitions to the previously described placed state  2409 . The resource-exchange state transitions from the placement-failure state  2422  to the previously described placement-request-received state  2415 . 
     Of course, in each particular implementation of the resource-exchange system, there may be many additional states and state transitions. The currently described state-transition diagrams are intended to show those states and state transitions that are common to the reasonably large fraction of the various possible implementations of the resource-exchange system. 
       FIG.  24 C  provides a resource-provider-centric resource-exchange state-transition diagram. The resource provider is shown to inhabit an initial state  2430 . When the resource provider receives an information request, the resource-exchange state transitions to the information-requested state  2431  and then returns back to the initial state when the requested information is returned to the Client-Exchange system. Similarly, when the resource provider system receives a bid request, the resource-exchange state transitions briefly to the bid-request-received state  2432  before returning to the initial state following a transmission of a computed bid request back to the cloud-exchange system. When the resource-provider system receives a winning-bid notification from the Client-Exchange System, the resource-exchange state transitions to the winning-bid-notification-received state  2433 . In the winning-bid-notification-received state, the resource-provider computing facility exchanges communications with the cloud-exchange system and the local cloud-exchange instance within the resource consumer to coordinate the transfer of virtual-machine build information or migration of virtual machines to the resource provider. When the virtual machine is built by the resource provider, the resource-exchange state transitions to the build-information-received state  2434  and then to the previously described transferred state  2410  once the one or more virtual machines have been prepared for launch. The resource-exchange state transitions from the winning-bid-notification-received state  2433  to the transferred state  2410  directly when the one or more virtual machines are migrated to the resource provider. States  2410 - 2412  and  2403  are again shown in  FIG.  24 C , for completeness, but are not again described. Following termination of the execution of the one or more virtual machines, the resource-exchange state transitions to the host-post-termination state  2435 . In the host-post-termination state, the resource provider exchanges communications with the cloud-exchange system to inform the cloud-exchange system of the execution termination and of the accrued fees for hosting the one or more virtual machines, cooperates with other entities to migrate the one or more virtual machines to another computing facility, in the case that the one or more virtual machines will continue to execute following lease termination or eviction, and cleans up local resources allocated for executing the one or more virtual machines within the resource-provider computing facility. 
     Note that the resource-exchange state is generally a combination of two or more of the states, discussed above with reference to  FIGS.  24 A-C , each inhabited by one or more of the resource consumer, the cloud-exchange system, and one or more resource providers. For example, the resource-exchange state may temporarily be a combination of the host-post-termination state  2435 , the placement-request-receive state  2415 , and the buy-policy-assigned state  2403 . Note also that certain of the operations performed to affect state transitions may vary, depending on the history of state transitions for a particular resource exchange. As one example, an active search context needs only to be allocated the first time a resource exchange transitions from the placement-request-receive state  2415  to the active-search-context-initialize state  2417 . 
     Although the present invention has been described in terms of particular embodiments, it is not intended that the invention be limited to these embodiments. Modifications within the spirit of the invention will be apparent to those skilled in the art. For example, many different design and implementation parameters can be varied to produce alternative implementations, including choice of operating system, the hardware platforms and virtualization layers that are controlled by the distributed service-based application, modular organization, control structures, data structures, and other such parameters. As mentioned above, particular implementations may use additional, fewer, or different states and state transitions then used by the above-described implementations. More complex virtual-machine placement and execution processes that involve placement of virtual machines concurrently into different hosts may involve somewhat more complex states and state transitions. The resource-exchange states may be digitally encoded and included in a resource-exchange context in a variety of different ways depending on implementation details. 
     It is appreciated that the previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.