PATENT ABSTRACT
This disclosure is directed to methods and systems to evaluate resource allocation costs of a data center. Methods and systems compute resource allocation costs of a cloud computing industry to obtain industry benchmarks that are compared with the resource allocation costs of the data center. The comparisons enable IT managers to objectively identify computational resource shortages, resource over investments, and where future investment in computational resources should be made for the data center.

PATENT DESCRIPTION
TECHNICAL FIELD 
       [0001]    The present disclosure is directed to methods and systems to evaluate resource allocation costs of a data center with respect to resource allocation costs of a cloud computing industry. 
       BACKGROUND 
       [0002]    In recent years, enterprises have shifted much of their computing needs from enterprise owned and operated computer systems to cloud computing providers. Cloud computing providers charge enterprises to store and run their applications in a cloud-computing facility and allow enterprises to purchase other computing services in much the same way utility customers purchase a service from a public utility. A typical cloud-computing facility is composed of numerous racks of servers, switches, routers, and mass data-storage devices interconnected by local-area networks, wide-area networks, and wireless communications that may be consolidated into a single data center or distributed geographically over a number of data centers. Enterprises typically run their applications in a cloud-computing facility as virtual machines (“VMs”) that are consolidated into a virtual data center (“VDC”) also called a software defined data center (“SDDC”). A VDC recreates the architecture and functionality of a physical data center for running an enterprise&#39;s applications. Because the vast numbers of VDCs and dynamic nature of VDCs running in a typical cloud-computing facility, VDC&#39;s introduce management challenges to information technology (“IT”) managers. Many IT managers lack the insight needed to objectively identify computational resource shortages and where future investment in computational resources should be made. 
       SUMMARY 
       [0003]    This disclosure is directed to methods and systems to evaluate resource allocation costs of a data center. Methods and systems compute resource allocation costs of a cloud computing industry to obtain industry benchmarks that are compared with the resource allocation costs of the data center. The comparisons enable IT managers to objectively identify computational resource shortages, resource over investments, and where future investment in computational resources should be made for the data center. 
     
    
     
       DESCRIPTION OF THE DRAWINGS 
         [0004]      FIG. 1  shows a general architectural diagram for various types of computers. 
           [0005]      FIG. 2  shows an Internet-connected distributed computer system. 
           [0006]      FIG. 3  shows cloud computing. 
           [0007]      FIG. 4  shows generalized hardware and software components of a general-purpose computer system. 
           [0008]      FIGS. 5A-5B  show two types of virtual machine and virtual-machine execution environments. 
           [0009]      FIG. 6  shows an example of an open virtualization format package. 
           [0010]      FIG. 7  shows virtual data centers provided as an abstraction of underlying physical-data-center hardware components. 
           [0011]      FIG. 8  shows virtual-machine components of a virtual-data-center management server and physical servers of a physical data center. 
           [0012]      FIG. 9  shows a cloud-director level of abstraction. 
           [0013]      FIG. 10  shows virtual-cloud-connector nodes. 
           [0014]      FIG. 11  shows an example of a system to collect cost information from physical data centers that combined represents a cloud computing industry. 
           [0015]      FIGS. 12A-12C  show examples of preprocessing the resource utilization data produced by physical data centers. 
           [0016]      FIG. 13  shows a data center and associated resource costs. 
           [0017]      FIG. 14  shows a control-flow diagram of a method to evaluate data center resource allocation costs of a data center. 
           [0018]      FIG. 15  shows a control-flow diagram of the method “compute resource allocation cost of industry benchmarks” called in  FIG. 14 . 
           [0019]      FIG. 16  shows a control-flow diagram of the method “compute data center resource allocation costs” called in  FIG. 14 . 
           [0020]      FIGS. 17A-17B  show a control-flow diagram of the method “compute resource allocation gaps” called in  FIG. 14 . 
           [0021]      FIG. 18  shows a control-flow diagram of the method “compute monetary impact of gaps” called in  FIG. 14 . 
       
    
    
     DETAILED DESCRIPTION 
       [0022]    A general description of physical data centers, hardware, virtualization, virtual machines, and virtual data centers are provided in a first subsection. Computational methods and system to evaluate resource allocation costs of a data center with respect to resource allocation cost of a cloud computing industry are provided in a second subsection. 
       Computer Hardware, Complex Computational Systems, and Virtualization 
       [0023]    The term “abstraction” is not, in any way, intended to mean or suggest an abstract idea or concept. Computational abstractions are tangible, physical interfaces that are implemented, ultimately, using physical computer hardware, data-storage devices, and communications systems. Instead, the term “abstraction” refers, in the current discussion, to a logical level of functionality encapsulated within one or more concrete, tangible, physically-implemented computer systems with defined interfaces through which electronically-encoded data is exchanged, process execution launched, and electronic services are provided. Interfaces may include graphical and textual data displayed on physical display devices as well as computer programs and routines that control physical computer processors to carry out various tasks and operations and that are invoked through electronically implemented application programming interfaces (“APIs”) and other electronically implemented interfaces. There is a tendency among those unfamiliar with modern technology and science to misinterpret the terms “abstract” and “abstraction,” when used to describe certain aspects of modern computing. For example, one frequently encounters assertions that, because a computational system is described in terms of abstractions, functional layers, and interfaces, the computational system is somehow different from a physical machine or device. Such allegations are unfounded. One only needs to disconnect a computer system or group of computer systems from their respective power supplies to appreciate the physical, machine nature of complex computer technologies. One also frequently encounters statements that characterize a computational technology as being “only software,” and thus not a machine or device. Software is essentially a sequence of encoded symbols, such as a printout of a computer program or digitally encoded computer instructions sequentially stored in a file on an optical disk or within an electromechanical mass-storage device. Software alone can do nothing. It is only when encoded computer instructions are loaded into an electronic memory within a computer system and executed on a physical processor that so-called “software implemented” functionality is provided. The digitally encoded computer instructions are an essential and physical control component of processor-controlled machines and devices, no less essential and physical than a cam-shaft control system in an internal-combustion engine. Multi-cloud aggregations, cloud-computing services, virtual-machine containers and VMs, communications interfaces, and many of the other topics discussed below are tangible, physical components of physical, electro-optical-mechanical computer systems. 
         [0024]      FIG. 1  shows a general architectural diagram for various types of computers. Computers that receive, process, and store event messages may be described by the general architectural diagram shown in  FIG. 1 , for example. The computer system contains one or multiple central processing units (“CPUs”)  102 - 105 , one or more electronic memories  108  interconnected with the CPUs by a CPU/memory-subsystem bus  110  or multiple busses, a first bridge  112  that interconnects the CPU/memory-subsystem bus  110  with additional busses  114  and  116 , 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  118 , and with one or more additional bridges  120 , which are interconnected with high-speed serial links or with multiple controllers  122 - 127 , such as controller  127 , that provide access to various different types of mass-storage devices  128 , electronic displays, input devices, and other such components, subcomponents, and computational devices. 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. 
         [0025]    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. 
         [0026]      FIG. 2  shows 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. 2  shows a typical distributed system in which a large number of PCs  202 - 205 , a high-end distributed mainframe system  210  with a large data-storage system  212 , and a large computer center  214  with large numbers of rack-mounted servers or blade servers all interconnected through various communications and networking systems that together comprise the Internet  216 . Such distributed computing systems provide diverse arrays of functionalities. For example, a PC user 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. 
         [0027]    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. 
         [0028]      FIG. 3  shows 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. 3 , a system administrator for an organization, using a PC  302 , accesses the organization&#39;s private cloud  304  through a local network  306  and private-cloud interface  308  and also accesses, through the Internet  310 , a public cloud  312  through a public-cloud services interface  314 . The administrator can, in either the case of the private cloud  304  or public cloud  312 , 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  316 . 
         [0029]    Cloud-computing facilities are intended to provide computational bandwidth and data-storage services much as utility companies provide electrical power and water to consumers. Cloud computing provides enormous advantages to small organizations without the devices to purchase, manage, and maintain in-house data centers. Such organizations can dynamically add and delete virtual computer systems from their virtual data centers within public clouds in order to track computational-bandwidth and data-storage needs, rather than purchasing sufficient computer systems within a physical data center to handle peak computational-bandwidth and data-storage demands. Moreover, small organizations can completely avoid the overhead of maintaining and managing physical computer systems, including hiring and periodically retraining information-technology specialists and continuously paying for operating-system and database-management-system upgrades. Furthermore, cloud-computing interfaces allow for easy and straightforward configuration of virtual computing facilities, flexibility in the types of applications and operating systems that can be configured, and other functionalities that are useful even for owners and administrators of private cloud-computing facilities used by a single organization. 
         [0030]      FIG. 4  shows 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 . The computer system  400  is often considered to include three fundamental layers: (1) a hardware layer or level  402 ; (2) an operating-system layer or level  404 ; and (3) an application-program layer or level  406 . The hardware layer  402  includes one or more processors  408 , system memory  410 , various different types of input-output (“I/O”) devices  410  and  412 , and mass-storage devices  414 . 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  404  interfaces to the hardware level  402  through a low-level operating system and hardware interface  416  generally comprising a set of non-privileged computer instructions  418 , a set of privileged computer instructions  420 , a set of non-privileged registers and memory addresses  422 , and a set of privileged registers and memory addresses  424 . In general, the operating system exposes non-privileged instructions, non-privileged registers, and non-privileged memory addresses  426  and a system-call interface  428  as an operating-system interface  430  to application programs  432 - 436  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  442 , memory management  444 , a file system  446 , device drivers  448 , 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 different 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 devices and other system devices 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  436  facilitates abstraction of mass-storage-device and memory devices as a high-level, easy-to-access, file-system interface. 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. 
         [0031]    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. 
         [0032]    For all of these reasons, a higher level of abstraction, referred to as the “virtual machine,” (“VM”) 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. 5A-B  show two types of VM and virtual-machine execution environments.  FIGS. 5A-B  use the same illustration conventions as used in  FIG. 4 .  FIG. 5A  shows a first type of virtualization. The computer system  500  in  FIG. 5A  includes the same hardware layer  502  as the hardware layer  402  shown in  FIG. 4 . However, rather than providing an operating system layer directly above the hardware layer, as in  FIG. 4 , the virtualized computing environment shown in  FIG. 5A  features a virtualization layer  504  that interfaces through a virtualization-layer/hardware-layer interface  506 , equivalent to interface  416  in  FIG. 4 , to the hardware. The virtualization layer  504  provides a hardware-like interface  508  to a number of VMs, such as VM  510 , in a virtual-machine layer  511  executing above the virtualization layer  504 . Each VM 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  514  and guest operating system  516  packaged together within VM  510 . Each VM is thus equivalent to the operating-system layer  404  and application-program layer  406  in the general-purpose computer system shown in  FIG. 4 . Each guest operating system within a VM interfaces to the virtualization-layer interface  508  rather than to the actual hardware interface  506 . The virtualization layer  504  partitions hardware devices into abstract virtual-hardware layers to which each guest operating system within a VM interfaces. The guest operating systems within the VMs, in general, are unaware of the virtualization layer and operate as if they were directly accessing a true hardware interface. The virtualization layer  504  ensures that each of the VMs currently executing within the virtual environment receive a fair allocation of underlying hardware devices and that all VMs receive sufficient devices to progress in execution. The virtualization-layer interface  508  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 VM that includes a guest operating system designed for a particular computer architecture to run on hardware of a different architecture. The number of VMs need not be equal to the number of physical processors or even a multiple of the number of processors. 
         [0033]    The virtualization layer  504  includes a virtual-machine-monitor module  518  (“VMM”) that virtualizes physical processors in the hardware layer to create virtual processors on which each of the VMs executes. For execution efficiency, the virtualization layer attempts to allow VMs to directly execute non-privileged instructions and to directly access non-privileged registers and memory. However, when the guest operating system within a VM accesses virtual privileged instructions, virtual privileged registers, and virtual privileged memory through the virtualization-layer interface  508 , the accesses result in execution of virtualization-layer code to simulate or emulate the privileged devices. The virtualization layer additionally includes a kernel module  520  that manages memory, communications, and data-storage machine devices on behalf of executing VMs (“VM kernel”). The VM kernel, for example, maintains shadow page tables on each VM 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  504  essentially schedules execution of VMs much like an operating system schedules execution of application programs, so that the VMs each execute within a complete and fully functional virtual hardware layer. 
         [0034]      FIG. 5B  shows a second type of virtualization. In  FIG. 5B , the computer system  540  includes the same hardware layer  542  and operating system layer  544  as the hardware layer  402  and the operating system layer  404  shown in  FIG. 4 . Several application programs  546  and  548  are shown running in the execution environment provided by the operating system  544 . In addition, a virtualization layer  550  is also provided, in computer  540 , but, unlike the virtualization layer  504  discussed with reference to  FIG. 5A , virtualization layer  550  is layered above the operating system  544 , 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  550  comprises primarily a VMM and a hardware-like interface  552 , similar to hardware-like interface  508  in  FIG. 5A . The virtualization-layer/hardware-layer interface  552 , equivalent to interface  416  in  FIG. 4 , provides an execution environment for a number of VMs  556 - 558 , each including one or more application programs or other higher-level computational entities packaged together with a guest operating system. 
         [0035]    In  FIGS. 5A-5B , the layers are somewhat simplified for clarity of illustration. For example, portions of the virtualization layer  550  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. 
         [0036]    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. 
         [0037]    A VM 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 VM within one or more data files.  FIG. 6  shows an OVF package. An OVF package  602  includes an OVF descriptor  604 , an OVF manifest  606 , an OVF certificate  608 , one or more disk-image files  610 - 611 , and one or more device files  612 - 614 . The OVF package can be encoded and stored as a single file or as a set of files. The OVF descriptor  604  is an XML document  620  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  622  and  623 . The next-level element includes a reference element  626  that includes references to all files that are part of the OVF package, a disk section  628  that contains meta information about all of the virtual disks included in the OVF package, a networks section  630  that includes meta information about all of the logical networks included in the OVF package, and a collection of virtual-machine configurations  632  which further includes hardware descriptions of each VM  634 . 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  606  is a list of cryptographic-hash-function-generated digests  636  of the entire OVF package and of the various components of the OVF package. The OVF certificate  608  is an authentication certificate  640  that includes a digest of the manifest and that is cryptographically signed. Disk image files, such as disk image file  610 , are digital encodings of the contents of virtual disks and device files  612  are digitally encoded content, such as operating-system images. A VM or a collection of VMs 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 VMs that is encoded within an OVF package. 
         [0038]    The advent of VMs and virtual environments has alleviated many of the difficulties and challenges associated with traditional general-purpose computing. Machine and operating-system dependencies can be significantly reduced or entirely eliminated by packaging applications and operating systems together as VMs and virtual appliances that execute within virtual environments provided by virtualization layers running on many different types of computer hardware. A next level of abstraction, referred to as virtual data centers or virtual infrastructure, provide a data-center interface to virtual data centers computationally constructed within physical data centers. 
         [0039]      FIG. 7  shows virtual data centers provided as an abstraction of underlying physical-data-center hardware components. In  FIG. 7 , a physical data center  702  is shown below a virtual-interface plane  704 . The physical data center consists of a virtual-data-center management server  706  and any of various different computers, such as PCs  708 , 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  710 , that are coupled together by local area networks, such as local area network  712  that directly interconnects server computer  710  and  714 - 720  and a mass-storage array  722 . The physical data center shown in  FIG. 7  includes three local area networks  712 ,  724 , and  726  that each directly interconnects a bank of eight servers and a mass-storage array. The individual server computers, such as server computer  710 , each includes a virtualization layer and runs multiple VMs. 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-interface plane  704 , a logical abstraction layer shown by a plane in  FIG. 7 , abstracts the physical data center to a virtual data center comprising one or more device pools, such as device pools  730 - 732 , one or more virtual data stores, such as virtual data stores  734 - 736 , and one or more virtual networks. In certain implementations, the device pools abstract banks of physical servers directly interconnected by a local area network. 
         [0040]    The virtual-data-center management interface allows provisioning and launching of VMs with respect to device 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 VMs. Furthermore, the virtual-data-center management server  706  includes functionality to migrate running VMs from one physical server to another in order to optimally or near optimally manage device allocation, provide fault tolerance, and high availability by migrating VMs to most effectively utilize underlying physical hardware devices, to replace VMs disabled by physical hardware problems and failures, and to ensure that multiple VMs 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 VMs and virtual appliances as well as to provide high-level, distributed functionalities that involve pooling the devices of individual physical servers and migrating VMs among physical servers to achieve load balancing, fault tolerance, and high availability. 
         [0041]      FIG. 8  shows virtual-machine components of a virtual-data-center management server and physical servers of a physical data center above which a virtual-data-center interface is provided by the virtual-data-center management server. The virtual-data-center management server  802  and a virtual-data-center database  804  comprise the physical components of the management component of the virtual data center. The virtual-data-center management server  802  includes a hardware layer  806  and virtualization layer  808 , and runs a virtual-data-center management-server VM  810  above the virtualization layer. Although shown as a single server in  FIG. 8 , the virtual-data-center management server (“VDC management server”) may include two or more physical server computers that support multiple VDC-management-server virtual appliances. The VM  810  includes a management-interface component  812 , distributed services  814 , core services  816 , and a host-management interface  818 . The management interface  818  is accessed from any of various computers, such as the PC  708  shown in  FIG. 7 . The management interface  818  allows the virtual-data-center administrator to configure a virtual data center, provision VMs, collect statistics and view log files for the virtual data center, and to carry out other, similar management tasks. The host-management interface  818  interfaces to virtual-data-center agents  824 ,  825 , and  826  that execute as VMs within each of the physical servers of the physical data center that is abstracted to a virtual data center by the VDC management server. 
         [0042]    The distributed services  814  include a distributed-device scheduler that assigns VMs to execute within particular physical servers and that migrates VMs in order to most effectively make use of computational bandwidths, data-storage capacities, and network capacities of the physical data center. The distributed services  814  further include a high-availability service that replicates and migrates VMs in order to ensure that VMs continue to execute despite problems and failures experienced by physical hardware components. The distributed services  814  also include a live-virtual-machine migration service that temporarily halts execution of a VM, encapsulates the VM in an OVF package, transmits the OVF package to a different physical server, and restarts the VM on the different physical server from a virtual-machine state recorded when execution of the VM was halted. The distributed services  814  also include a distributed backup service that provides centralized virtual-machine backup and restore. 
         [0043]    The core services  816  provided by the VDC management server  810  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 device-management module. Each physical server  820 - 822  also includes a host-agent VM  828 - 830  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  824 - 826  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 device allocations made by the VDC management server  810 , 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. 
         [0044]    The virtual-data-center abstraction provides a convenient and efficient level of abstraction for exposing the computational devices of a cloud-computing facility to cloud-computing-infrastructure users. A cloud-director management server exposes virtual devices of a cloud-computing facility to cloud-computing-infrastructure users. In addition, the cloud director introduces a multi-tenancy layer of abstraction, which partitions 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. 
         [0045]      FIG. 9  shows a cloud-director level of abstraction. In  FIG. 9 , three different physical data centers  902 - 904  are shown below planes representing the cloud-director layer of abstraction  906 - 908 . Above the planes representing the cloud-director level of abstraction, multi-tenant virtual data centers  910 - 912  are shown. The devices 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  910  is partitioned into four different tenant-associated virtual-data centers within a multi-tenant virtual data center for four different tenants  916 - 919 . Each multi-tenant virtual data center is managed by a cloud director comprising one or more cloud-director servers  920 - 922  and associated cloud-director databases  924 - 926 . Each cloud-director server or servers runs a cloud-director virtual appliance  930  that includes a cloud-director management interface  932 , a set of cloud-director services  934 , and a virtual-data-center management-server interface  936 . The cloud-director services include an interface and tools for provisioning multi-tenant 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 VMs that each contains an OS and/or one or more VMs containing applications. A template may include much of the detailed contents of VMs and virtual appliances that are encoded within OVF packages, so that the task of configuring a VM 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. 
         [0046]    Considering  FIGS. 7 and 9 , the VDC-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. 
         [0047]      FIG. 10  shows 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. 10 , seven different cloud-computing facilities are shown  1002 - 1008 . Cloud-computing facility  1002  is a private multi-tenant cloud with a cloud director  1010  that interfaces to a VDC management server  1012  to provide a multi-tenant private cloud comprising multiple tenant-associated virtual data centers. The remaining cloud-computing facilities  1003 - 1008  may be either public or private cloud-computing facilities and may be single-tenant virtual data centers, such as virtual data centers  1003  and  1006 , multi-tenant virtual data centers, such as multi-tenant virtual data centers  1004  and  1007 - 1008 , or any of various different kinds of third-party cloud-services facilities, such as third-party cloud-services facility  1005 . An additional component, the VCC server  1014 , acting as a controller is included in the private cloud-computing facility  1002  and interfaces to a VCC node  1016  that runs as a virtual appliance within the cloud director  1010 . A VCC server may also run as a virtual appliance within a VDC management server that manages a single-tenant private cloud. The VCC server  1014  additionally interfaces, through the Internet, to VCC node virtual appliances executing within remote VDC management servers, remote cloud directors, or within the third-party cloud services  1018 - 1023 . The VCC server provides a VCC server interface that can be displayed on a local or remote terminal, PC, or other computer system  1026  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. 
       Computational Methods and System to Evaluate Resource Allocation Costs of a Data Center with Respect to Resource Allocation Cost of a Cloud Computing Industry 
       [0048]      FIG. 11  shows an example of a system to collect computational resource costs from M separate physical data centers that combined represents a cloud computing industry. The resources may be CPUs, memory, and data storage. Each of the M physical data centers may be configured as described above with reference to  FIG. 7  to run one or more VDCs as described above with reference to  FIG. 9 . Each physical data center generates log files, configuration files, resource utilization data, such as usage data regarding CPU&#39;s, memory, and data storage, and stores the data in one or more data-storage devices. For example, CPU utilization, memory utilization, and data storage utilization by the VMs that run in each of the M physical data centers may be recorded periodically, such as daily, weekly, or monthly. Each of the M physical data centers also compute a total VDC cost of running one or more VDCs. The resource utilization data and total VDC costs may be sent via the Internet  1101  to a cloud computing service facility  1102  that stores the resource utilization data and total VDC cost. A data center  1103  accesses the resource utilization data and total VDC costs of the M physical data centers maintained by the cloud compute service facility  1102  in order to compute resource allocation costs of the cloud computing industry. The resource allocation costs serve as cloud computing industry benchmarks that may be compared with resource allocation costs of the data center  1103 . Differences between the allocation cost of the data center  1103  and the allocation costs of the cloud computing industry may be used to adjust operations of the data center  1103  in order to shift allocation cost and total VDC cost of the data center  1103  into closer alignment with the allocation costs and total VDC costs of the cloud computing industry. 
         [0049]    The resource utilization data and total VDC costs of the M physical data centers maintained by the cloud computing services facility  1102  may be sent to the data center  1103  on a regular basis, such as daily, weekly, or monthly. The data center  1103  stores the resource utilization data and total VDC costs in a data-storage device  1104 . In the example of  FIG. 11 , the data center  1103  runs three VDC&#39;s, such as VDC  1105 . One or more of the VDC&#39;s may form a private cloud. In the example of  FIG. 11 , up to three private clouds may be run in the data center  1103 . 
         [0050]    The resource utilization data and total VDC costs of the M physical data centers stored in the one or more data-storage devices  1104  are preprocessed to organize the resource utilization data and total VDC costs. The resource utilization data includes CPU utilization, number of CPU cores, memory utilization, memory capacity, storage utilization, and storage capacity for each of the data centers that collectively comprise a cloud computing industry resource utilization data.  FIGS. 12A-12C  show examples of preprocessing the resource utilization data produced by each of the M physical data centers. In the example of  FIG. 12A , the number of CPU cores and CPU utilization of the VMs that run in the M physical data centers are collected. CPU utilization is the amount of time a CPU was used for processing instructions of one or more VMs. The number of CPU cores used by VMs run in the m-th data center are denoted by No.CPUCore m  and the CPU utilization of the VMs that run in the m-th data center is denoted by CPUUtilization m , where the index m=1, . . . , M. In the example of  FIG. 12B , the memory utilization and memory capacity of each of the M data centers are collected. Memory includes any of various different kinds of random access memory (“RAM”). Memory capacity of the m-th data center is denoted by MemCapacity m  and memory utilization of the m-th data center is denoted by MemUtilization m . The MemCapacity m  is the amount of memory available in the m-th data center and MemUtilization m  is to the actual amount of memory used by the VMs that run in the m-th data center. In the example of  FIG. 12C , the data storage utilization and data storage capacity of each of the M data centers are collected. The data storage capacity of the m-th data center is denoted by StorCapacity m  and the data storage utilization of the m-th data center is denoted by StorUtilization m . The StorCapacity m  is the total amount of data storage available in the data-storage devices of the m-th data center, and StorUtilization m  is to the amount of data storage used to store data generated by the VMs that run in the m-th data center. 
         [0051]    The resource utilization data and total VDC costs of the M data centers may be used to calculate resource allocation cost industry benchmarks that may be compared with resource allocation costs of a data center, such as the data center  1103 . Allocation cost refers to the VDC costs associated with running VMs, and unallocated costs refers to the VDC cost not associated with running VMs (e.g., unused hardware and unused labor). 
         [0052]    CPU allocation cost industry benchmarks (“IBs”) are computed as follows. A total CPU utilization of the M physical data centers is computed by summing the CPU utilization of each of the M physical data centers: 
         [0000]    
       
         
           
             
               
                 
                   
                     TotalCPUUtil 
                      
                     
                       ( 
                       IB 
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         m 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       CPUUtilization 
                       m 
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
         [0000]    A CPU allocation cost of the cloud computing industry may be computed as follows: 
         [0000]      CPUAlloCost(IB)=TotalCPUUtil(IB)×CPU_base_rate  (2)
 
         [0053]    where CPU_base_rate is the cost per unit of CPU utilization (e.g., dollars per unit of time). 
         [0054]    The CPU allocation cost of Equation (2) is the cost of CPU utilization across the cloud computing industry. A total CPU capacity of the cloud computing industry may be computed as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     TotalCPUCap 
                      
                     
                       ( 
                       IB 
                       ) 
                     
                   
                   = 
                   
                     
                       ( 
                       
                         
                           ∑ 
                           
                             m 
                             = 
                             1 
                           
                           M 
                         
                          
                         
                           No 
                           . 
                           
                             CPUCores 
                             m 
                           
                         
                       
                       ) 
                     
                     × 
                     CPU_speed 
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
         [0055]    where CPU_speed may be an average CPU speed per core. 
         [0000]    A total CPU cost of the cloud computing industry may be computed from the total CPU capacity of Equation (3) and the CPU base rate as follows: 
         [0000]      TotalCPUCost(IB)=TotalCPUCap(IB)×CPU_base_rate  (4)
 
         [0000]    The total CPU cost of Equation (4) is the total cost of CPUs across of the cloud computing industry. The portion of cost allocated to CPU usage in the cloud computing industry to the total cost of CPU capacity in the cloud computing industry may be calculated as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     CPUAlloFrac 
                      
                     
                       ( 
                       IB 
                       ) 
                     
                   
                   = 
                   
                     
                       CPUAlloCost 
                        
                       
                         ( 
                         IB 
                         ) 
                       
                     
                     
                       TotalCPUCost 
                        
                       
                         ( 
                         IB 
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
         [0000]    The CPU allocation fraction given by Equation (5) represents the fraction or proportion of total cost of CPUs in the cloud computing industry that is attributed to CPU allocated cost, which may also be represented as a percentage. 
         [0056]    The CPU allocation cost IBs computed in Equations (1)-(5) may be compared with associated CPU allocation costs of a data center, such as the data center  1103 .  FIG. 13  shows the data center  1103  and three VDCs. The total CPU utilization, TotalCPUUtil(DC), by the VMs comprising the three VDCs of the data center  1103  may be used to compute the CPU allocation cost for the data center  1103  as follows: 
         [0000]      CPUAlloCost(DC)=TotalCPUUtil(DC)×CPU_base_rate  (6)
 
         [0000]    The number of CPU cores in the data center  1103 , No.CPUCores(DC), may be used to compute the total CPU capacity of the data center  1103  as follows: 
         [0000]      TotalCPUCap(DC)=No.CPUCores(DC)×CPU_speed  (7)
 
         [0000]    The total CPU total of the CPU cores in the data center  1103  may calculated as follows: 
         [0000]      TotalCPUCost(DC)=TotalCPUCap(DC)×CPU_base_rate  (8)
 
         [0000]    The fraction of cost allocated to CPU usage in the data center  1103  of the total cost of CPU capacity of the data center  1103  may be calculated as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     CPUAlloFrac 
                      
                     
                       ( 
                       
                         D 
                          
                         
                             
                         
                          
                         C 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       CPUAlloCost 
                        
                       
                         ( 
                         
                           D 
                            
                           
                               
                           
                            
                           C 
                         
                         ) 
                       
                     
                     
                       TotalCPUCost 
                        
                       
                         ( 
                         
                           D 
                            
                           
                               
                           
                            
                           C 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
     
         [0000]    The CPU allocation fraction given by Equation (9) represents the fraction or proportion of total cost of CPUs in the data center  1103  that is attributed to CPU allocated cost, which may also be represented as a percentage. 
         [0057]    The difference between the CPU allocation fraction of the data center  1103  given by Equation (9) and the CPU allocation fraction of the cloud computing industry given by Equation (5) is computed as follows: 
         [0000]      CPUAlloGap=CPUAlloFrac(IB)−CPUAlloFrac(DC)  (10)
 
         [0000]    The CPU allocation gap of Equation (10) represents the degree to which cost attributed to CPU allocation in the data center  1103  differs from the cost attributed to CPU allocation across the cloud computing industry. 
         [0058]    A CPU threshold, T CPU , may be used to assess the degree to which CPU allocation cost in the data center  1103  are aligned with CPU allocation cost across the cloud computing industry. When 
         [0000]      |CPUAlloGap|≦ T   CPU   (11)
 
         [0000]    the cost attributed to CPU allocation in the data center  1103  is considered closely aligned with the cost attributed to CPU allocation across the cloud computing industry. 
         [0059]    On the other hand, when 
         [0000]      |CPUAlloGap|&gt; T   CPU   (12)
 
         [0000]    the cost attributed to CPU allocation in the data center  1103  is not considered closely aligned with the cost attributed to CPU allocation across the cloud computing industry. In this case, if CPUAlloGap&gt;0, then investment in additional processors may be a next area of growth investment for the data center. If CPUAlloGap&lt;0, then the investment in processors exceeds that of the cloud computing industry, which may be an indication of CPU wastage, and no further investment in processors should be made. 
         [0060]    The monetary impact of the gap between the cost of CPU allocation of the data center  1103  and the cost of CPU allocation across the cloud computing industry may be computed as follows: 
         [0000]      MonetaryCPUAlloImpact=CPUAlloGap×TotalCPUCost(DC)  (13)
 
         [0000]    The monetary CPU allocation impact computed according to Equation (13) is a monetary value of the degree to which the cost of CPU allocation for the data center  1103  is less than or greater than the cost of CPU allocation for the cloud computing industry. MonetaryCPUAlloImpact&lt;0 may be used as an indicator of CPU cost wastage, and MonetarCPUAlloImpact&gt;0 may be used as an indicator of how much money should be invested in processors. 
         [0061]    Memory allocation cost IBs are computed as follows. A total memory utilization of the M physical data centers is computed by summing the memory utilization of each of the M physical data centers: 
         [0000]    
       
         
           
             
               
                 
                   
                     TotalMemUtil 
                      
                     
                       ( 
                       IB 
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         m 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       MemUtilization 
                       m 
                     
                   
                 
               
               
                 
                   ( 
                   14 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Memory allocation cost of the cloud computing industry may be computed as follows: 
         [0000]      MemAlloCost(IB)=MemTotalUtil(IB)×Mem_base_rate  (15)
 
         [0062]    where Mem_base_rate is the cost per number of bytes of memory (e.g., gigabytes). The memory allocation cost of Equation (15) is the cost of memory utilization across the cloud computing industry. A total memory capacity of the cloud computing industry may be computed summing the memory capacity of each of the M physical data centers as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     TotalMemCap 
                      
                     
                       ( 
                       IB 
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         m 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       MemCapacity 
                       m 
                     
                   
                 
               
               
                 
                   ( 
                   16 
                   ) 
                 
               
             
           
         
       
     
         [0000]    A total memory cost of the cloud computing industry may be computed from the total memory capacity of Equation (16) and the memory base rate as follows: 
         [0000]      TotalMemCost(IB)=TotalMemCap(IB)×Mem_base_rate  (17)
 
         [0000]    The total memory cost of Equation (17) is the total cost of memory across of the cloud computing industry. The portion of cost allocated to memory in the cloud computing industry to the total cost of memory capacity in the cloud computing industry may be calculated as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     MemAlloFrac 
                      
                     
                       ( 
                       IB 
                       ) 
                     
                   
                   = 
                   
                     
                       MemAlloCost 
                        
                       
                         ( 
                         IB 
                         ) 
                       
                     
                     
                       TotalMemCost 
                        
                       
                         ( 
                         IB 
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   18 
                   ) 
                 
               
             
           
         
       
     
         [0000]    The memory allocation fraction given by Equation (18) represents the fraction or proportion of total cost of memory in the cloud computing industry that is attributed to memory allocation cost, which may also be represented as a percentage. 
         [0063]    The memory allocation cost IBs computed in Equations (14)-(18) may be compared with associated memory allocation costs of a data center, such as the data center  1103 . Returning to  FIG. 13 , the total memory utilization, TotalMemUtil(DC), by the VMs of the three VDCs running in the data center  1103  may be used to compute the memory allocation cost for the data center  1103  as follows: 
         [0000]      MemAlloCost(DC)=TotalMemUtil(DC)×Mem_base_rate  (19)
 
         [0000]    The amount of memory in the data center  1103 , TotalMemCap(DC), may be used to compute the total memory cost associated with the data center  1103  as follows: 
         [0000]      TotalMemCost(DC)=TotalMemCap(DC)×Mem_base_rate  (20)
 
         [0000]    The fraction of cost allocated to memory usage in the data center  1103  of the total cost of memory capacity of the data center  1103  may be calculated as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     MemAlloFrac 
                      
                     
                       ( 
                       
                         D 
                          
                         
                             
                         
                          
                         C 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       MemAlloCost 
                        
                       
                         ( 
                         
                           D 
                            
                           
                               
                           
                            
                           C 
                         
                         ) 
                       
                     
                     
                       TotalMemCost 
                        
                       
                         ( 
                         
                           D 
                            
                           
                               
                           
                            
                           C 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   21 
                   ) 
                 
               
             
           
         
       
     
         [0000]    The memory allocation fraction given by Equation (21) represents the fraction or proportion of total cost of memory in the data center  1103  that is attributed to memory allocation cost, which may also be represented as a percentage. 
         [0064]    The difference between the memory allocation fraction of the data center  1103  given by Equation (21) and the memory allocation fraction of the cloud computing industry given by Equation (18) is computed as follows: 
         [0000]      MemAlloGap=MemAlloFrac( BM )−MemAlloFrac(DC)  (22)
 
         [0000]    The memory allocation gap of Equation (22) represents the degree to which cost attributed to memory allocation in the data center  1103  differs from the cost attributed to memory allocation across the cloud computing industry. 
         [0065]    A memory threshold, T Mem , may be used to assess the degree to which memory allocation cost in the data center  1103  are aligned with memory allocation cost across the cloud computing industry. When 
         [0000]      |MemAlloGap|≦ T   Mem   (23)
 
         [0000]    the cost attributed to memory allocation in the data center  1103  is considered closely aligned with the cost attributed to memory allocation across the cloud computing industry. On the other hand, when 
         [0000]      |MemAlloGap|&gt; T   Mem   (24)
 
         [0000]    the cost attributed to memory allocation in the data center  1103  is not considered closely aligned with the cost attributed to memory allocation across the cloud computing industry. In this case, if MemAlloGap&gt;0, then investment in additional memory may be a next area of growth investment for the data center. If MemAlloGap&lt;0, then the investment in memory exceeds that of the cloud computing industry, which may be an indication of wastage, and no further investment in memory should be made. 
         [0066]    The monetary impact of the gap between the cost of memory allocation of the data center  1103  and the cost of memory allocation across the cloud computing industry may be computed as follows: 
         [0000]      MonetaryMemAlloImpact=MemAlloGap×TotalMemCost(DC)  (25)
 
         [0000]    The monetary memory allocation impact computed according to Equation (25) is a monetary value of the degree to which the cost of memory allocation of the data center  1103  is less than or greater than the cost of memory allocation for the cloud computing industry. MonetaryMemAlloImpact&lt;0 may be used as an indicator of memory wastage, and MonetaryMemAlloImpact&gt;0 may be used as an indicator of how much money should be invested in memory. 
         [0067]    Data storage allocation cost IBs are computed as follows. A total data storage utilization of the M physical data centers is computed by summing the data storage utilization of each of the M physical data centers: 
         [0000]    
       
         
           
             
               
                 
                   
                     TotalStorUtil 
                      
                     
                       ( 
                       IB 
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         m 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       StorUtilization 
                       m 
                     
                   
                 
               
               
                 
                   ( 
                   26 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Data storage allocation cost of the cloud computing industry may be computed as follows: 
         [0000]      StorAlloCost(IB)=TotalStorUtil(IB)×Stor_base≦rate  (27)
 
         [0000]    where Stor_base_rate is the cost per number of bytes of data storage (e.g., gigabytes).
 
The data storage allocation cost of Equation (15) is the cost of data storage utilization across the cloud computing industry. A total data storage capacity of the cloud computing industry may be computed summing the data storage capacity of each of the M physical data centers as follows:
 
         [0000]    
       
         
           
             
               
                 
                   
                     TotalStorCap 
                      
                     
                       ( 
                       IB 
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         m 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       StorCapacity 
                       m 
                     
                   
                 
               
               
                 
                   ( 
                   28 
                   ) 
                 
               
             
           
         
       
     
         [0000]    A total data storage cost of the cloud computing industry may be computed from the total data storage capacity of Equation (16) and the data storage base rate as follows: 
         [0000]      TotalStorCost(IB)=TotalStorCap(IB)×Stor_base_rate  (29)
 
         [0000]    The total data storage cost of Equation (17) is the total cost of data storage across of the cloud computing industry. The portion of cost allocated to data storage in the cloud computing industry to the total cost of data storage capacity in the cloud computing industry may be calculated as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     StorAlloFrac 
                      
                     
                       ( 
                       IB 
                       ) 
                     
                   
                   = 
                   
                     
                       StorAlloCost 
                        
                       
                         ( 
                         IB 
                         ) 
                       
                     
                     
                       TotalStorCost 
                        
                       
                         ( 
                         IB 
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   30 
                   ) 
                 
               
             
           
         
       
     
         [0000]    The data storage allocation fraction given by Equation (18) represents the fraction or proportion of total cost of data storage in the cloud computing industry that is attributed to data storage allocation cost, which may also be represented as a percentage. 
         [0068]    The data storage allocation cost IBs computed in Equations (26)-(30) may be compared with associated data storage allocation costs of a data center, such as the data center  1103 . Returning to  FIG. 13 , the total data storage utilization, TotalStorUtil(DC), by the VMs of the three VDCs running in the data center  1103  may be used to compute the data storage allocation cost for the data center  1103  as follows: 
         [0000]      StorAlloCost(DC)=TotalStorUtil(DC)×Stor_base_rate  (31)
 
         [0000]    The amount of data storage in the data center  1103 , TotalStorCap(DC), may be used to compute the total data storage cost associated with the data center  1103  as follows: 
         [0000]      TotalStorCost(DC)=TotalStorCap(DC)×Stor_base_rate  (32)
 
         [0000]    The fraction of cost allocated to data storage usage in the data center  1103  of the total cost of data storage capacity of the data center  1103  may be calculated as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     StorAlloFrac 
                      
                     
                       ( 
                       
                         D 
                          
                         
                             
                         
                          
                         C 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       StorAlloCost 
                        
                       
                         ( 
                         
                           D 
                            
                           
                               
                           
                            
                           C 
                         
                         ) 
                       
                     
                     
                       TotalStorCost 
                        
                       
                         ( 
                         
                           D 
                            
                           
                               
                           
                            
                           C 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   33 
                   ) 
                 
               
             
           
         
       
     
         [0000]    The data storage allocation fraction given by Equation (33) represents the fraction or proportion of total cost of data storage in the data center  1103  that is attributed to data storage allocation cost, which may also be represented as a percentage. 
         [0069]    The difference between the data storage allocation fraction of the data center  1103  given by Equation (33) and the data storage allocation fraction of the cloud computing industry given by Equation (30) is computed as follows: 
         [0000]      StorAlloGap=StorAlloFrac(IB)−StorAlloFrac(DC)  (34)
 
         [0000]    The data storage allocation gap of Equation (34) represents the degree to which cost attributed to data storage allocation in the data center  1103  differs from the cost attributed to data storage allocation across the cloud computing industry. 
         [0070]    A storage threshold, T Stor , may be used to assess the degree to which data storage allocation cost in the data center  1103  are aligned with data storage allocation cost across the cloud computing industry. For example, when 
         [0000]      |StorAlloGap|≦ T   Stor   (35)
 
         [0000]    the cost attributed to data storage allocation in the data center  1103  is considered closely aligned with the cost attributed to data storage allocation across the cloud computing industry. When 
         [0000]      |StorAlloGap|&gt; T   Stor   (36)
 
         [0000]    the cost attributed to data storage allocation in the data center  1103  is not considered closely aligned with the cost attributed to data storage allocation across the cloud computing industry. In this case, if StorAlloGap&gt;0, then investment in additional data storage may be a next area of growth investment for the data center. If StorAlloGap&lt;0, then the investment in data storage exceeds that of the cloud computing industry, which may be an indication of wastage, and no further investment in data storage should be made. 
         [0071]    The monetary impact of the gap between the cost of data storage allocation of the data center  1103  and the cost of data storage allocation across the cloud computing industry may be computed as follows: 
         [0000]      MonetaryStorAlloImpact=StorAlloGap×TotalStorCost(DC)  (37)
 
         [0000]    The monetary data storage allocation impact computed according to Equation (37) is a monetary value of the degree to which the cost of data storage allocation for the data center  1103  is less than or greater than the cost of data storage allocation for the cloud computing industry. MonetaryStorAlloImpact&lt;0 may be used as an indicator of data storage wastage, and MonetarStorAlloImpact&gt;0 may be used as an indicator of how much money should be invested in data storage. 
         [0072]    A total resource allocation cost of CPUs, memory, data storage by the cloud computing industry may be calculated by summing the CPU allocation cost of Equation (6), the memory allocation cost of Equation (19), and the storage allocation cost (27) as follows: 
         [0000]      TotalAlloCost(IB)=CPUAlloCost(IB)+MemAlloCost(IB)+StorAlloCost(IB)  (38)
 
         [0000]    A total resource allocation fraction for the cloud computing industry may be computed as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       TotalAlloFrac 
                        
                       
                         ( 
                         IB 
                         ) 
                       
                     
                     = 
                     
                       
                         TotalAlloCost 
                          
                         
                           ( 
                           IB 
                           ) 
                         
                       
                       
                         TotalVDCCost 
                          
                         
                           ( 
                           IB 
                           ) 
                         
                       
                     
                   
                    
                   
                     
 
                   
                    
                   where 
                    
                   
                     
 
                   
                    
                   
                     
                       TotalVDCCost 
                        
                       
                         ( 
                         IB 
                         ) 
                       
                     
                     = 
                     
                       
                         ∑ 
                         
                           m 
                           = 
                           1 
                         
                         M 
                       
                        
                       
                         TotalVDCCost 
                         m 
                       
                     
                   
                 
               
               
                 
                   ( 
                   39 
                   ) 
                 
               
             
           
         
       
     
         [0000]    and TotalVDCCost m  is the total cost of one or more VDCs that run in the m-th physical data center. 
         [0073]    A total resource allocation cost of CPUs, memory, and data storage for the data center  1103  may be calculated by summing the CPU allocation cost of Equation (6), the memory allocation cost of Equation (19), and the storage allocation cost (27) as follows: 
         [0000]      TotalAlloCost(DC)=CPUAlloCost(DC)+MemAlloCost(DC)+StorAlloCost(DC)  (40)
 
         [0000]    A total resource allocation fraction for the data center  1103  may be computed as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     TotalAlloFrac 
                      
                     
                       ( 
                       
                         D 
                          
                         
                             
                         
                          
                         C 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       TotalAlloCost 
                        
                       
                         ( 
                         
                           D 
                            
                           
                               
                           
                            
                           C 
                         
                         ) 
                       
                     
                     
                       TotalVDCCost 
                        
                       
                         ( 
                         
                           D 
                            
                           
                               
                           
                            
                           C 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   41 
                   ) 
                 
               
             
           
         
       
     
         [0000]    where TotalVDCCost(DC) is the total cost of the three VDCs that run in the m-th physical data center. 
         [0074]    The difference between the resource allocation fraction of the data center  1103  given by Equation (33) and the resource allocation fraction of the cloud computing industry given by Equation (30) is computed as follows: 
         [0000]      TotalAlloGap=TotalAlloFrac(IB)−TotalAlloFrac(DC)  (42)
 
         [0000]    The resource allocation gap of Equation (34) represents the degree to which cost attributed to resource allocation in the data center  1103  differs from the cost attributed to resource allocation across the cloud computing industry. 
         [0075]    A total resource threshold, T Tot , may be used to assess the degree to which resource allocation cost in the data center  1103  are aligned with resource allocation cost across the cloud computing industry. For example, when 
         [0000]      |TotalAlloGap|≦ T   Tot   (43)
 
         [0000]    the cost attributed to resource allocation in the data center  1103  is considered closely aligned with the cost attributed to resource allocation across the cloud computing industry. On the other hand, when 
         [0000]      |TotalAlloGap|&gt; T   Tot   (44)
 
         [0000]    the cost attributed to resource allocation in the data center  1103  is not considered closely aligned with the cost attributed to resource allocation across the cloud computing industry. 
         [0076]    The monetary impact of the gap between the cost of resource allocation of the data center  1103  and the cost of resource allocation across the cloud computing industry may be computed as follows: 
         [0000]      MonetaryTotalAlloImpact=TotalAlloGap×TotalVDCCost(DC)  (45)
 
         [0000]    The monetary data resource allocation impact computed according to Equation (45) is a monetary value of the degree to which the cost of resource allocation for the data center  1103  is less than or greater than the cost of resource allocation for the cloud computing industry. 
         [0077]    Consider, for example, a cloud computing industry total allocation fraction of 0.60 or 60% (i.e., otalAlloFrac(IB)=0.60) computed according to Equation (39). Suppose total VDC cost for the data center  1103  is $1,000,000 (i.e., TotalVDCCost(DC)=$1,000,000) and total resource allocation cost for the VMs that run in the data center  1103  is $500,000 (i.e., TotalAlloCost(DC)=$500,000). The total resource allocation fraction for the data center  1103  computed according to Equation (41) is is 0.50 or 50% (i.e., TotalAlloFrac(DC)=0.50). The total resource allocation gap is 0.10 (i.e., TotalAlloGap=0.60−0.5=0.10). The monetary impact of the gap between the cost of resource allocation of the data center  1103  and the cost of resource allocation across the cloud computing industry is $100,000 (i.e. MonetaryTotalAlloImpact=0.10×$1,000,000=$100,000). 
         [0078]      FIG. 14  shows a control-flow diagram of a method to evaluate data center resource allocation costs of a data center. In block  1401 , resource utilization data is collected from a number of data centers that represents a cloud computing industry as described above with reference to  FIG. 11 . The resources may be computational resources, such as CPU&#39;s, memory, and data storage, of the data centers. The resource utilization data includes CPU utilization, number of CPU cores, memory utilization, memory capacity, storage utilization, and storage capacity for each of the data centers that collectively comprise a cloud computing industry resource utilization data, as described above with reference to  FIGS. 12A-12C . In block  1402 , the resource utilization data are pre-processed by sorted according to the type of resource, as described above with reference to  FIGS. 12A-12C . In block  1403 , a routine “compute resource allocation cost of industry benchmarks” is called to compute resource allocated costs for the data centers that are representative of the cloud computing industry. In block  1404 , a routine “compute data center resource allocation costs” is called to resource allocations for the resources of the data center. In block  1405 , a routine “compute resource allocation gaps” is called to compute gaps between resource allocation of the data center and the cloud computing industry. In block  1406 , a routine “compute monetary impact of gaps” is called to compute the monetary impact of the resource allocation gaps computed in block  1405 . 
         [0079]      FIG. 15  shows a control-flow diagram of the method “compute resource allocation cost of industry benchmarks” called in block  1403  of  FIG. 14 . In block  1501 , a CPU allocation cost is computed as described above with reference to Equations (1) and (2). In block  1502 , a total CPU cost is computed as described above with reference to Equations (3). In block  1503 , memory allocation cost is computed as described above with reference to Equations (14) and (15). In block  1504 , total memory cost is computed as described above with reference to Equations (16) and (17). In block  1505 , data storage allocation cost is computed as described above with reference to Equations (26) and (27). In block  1506 , total data storage cost is computed as described above with reference to Equations (28) and (29). In block  1507 , a total resource allocation cost is computed from the allocation cost computed in blocks  1501 ,  1503 , and  1505 , as described above with reference to Equation (38). 
         [0080]      FIG. 16  shows a control-flow diagram of the method “compute data center resource allocation costs” called in block  1404  of  FIG. 14 . In block  1601 , a CPU allocation cost is computed as described above with reference to Equation (6). In block  1602 , a total CPU cost is computed as described above with reference to Equations (8). In block  1603 , memory allocation cost is computed as described above with reference to Equation (19). In block  1604 , a total memory cost is computed as described above with reference to Equation (20). In block  1605 , data storage allocation cost is computed as described above with reference to Equation (31). In block  1606 , total data storage cost is computed as described above with reference to Equation (32). In block  1607 , a total resource allocation cost is computed from the allocation cost computed in blocks  1601 ,  1603 , and  1605 , as described above with reference to Equation (40). 
         [0081]      FIGS. 17A-17B  show a control-flow diagram of the method “compute resource allocation gaps” called in block  1405  of  FIG. 14 . In block  1701 , a CPU allocation fraction is computed for the cloud computing industry according to Equation (5) based on the CPU allocation cost and the total CPU cost computed in corresponding blocks  1501  and  1502  of  FIG. 15 . In block  1702 , a CPU allocation fraction is computed for the data center according to Equation (9) based on the CPU allocation cost and the total CPU cost computed in corresponding blocks  1601  and  1602  of  FIG. 16 . In block  1703 , a CPU allocation gap between the CPU allocation fractions computed in blocks  1701  and  1702  is computed as described above with reference to Equation (10). In decision block  1704 , the absolute value of the CPU allocation gap is greater than a CPU threshold, as described above with reference to Equation (12), control flows to block  1705 . Otherwise, control flows to block  1706 . In block  1705 , an alert is generated that indicates CPU allocation costs are not aligned with CPU allocation cost of the cloud computing industry. In block  1706 , a memory allocation fraction is computed for the cloud computing industry according to Equation (18) based on the memory allocation cost and the total memory cost computed in corresponding blocks  1503  and  1504  of  FIG. 15 . In block  1707 , a memory allocation fraction is computed for the data center according to Equation (21) based on the memory allocation cost and the total memory cost computed in corresponding blocks  1603  and  1604  of  FIG. 16 . In block  1708 , a memory allocation gap between the memory allocation fractions computed in blocks  1706  and  1707  is computed as described above with reference to Equation (22). In decision block  1709 , the absolute value of the memory allocation gap is greater than a memory threshold, as described above with reference to Equation (24), control flows to block  1710 . Otherwise, control flows to block  1711 . In block  1710 , an alert is generated that indicates memory allocation costs are not aligned with memory allocation cost of the cloud computing industry. In block  1711 , a data storage allocation fraction is computed for the cloud computing industry according to Equation (30) based on the data storage allocation cost and the total data storage cost computed in corresponding blocks  1505  and  1506  of  FIG. 15 . In block  1712 , a data storage allocation fraction is computed for the data center according to Equation (33) based on the data storage allocation cost and the total data storage cost computed in corresponding blocks  1605  and  1606  of  FIG. 16 . In block  1713 , a data storage allocation gap between the data storage allocation fractions computed in blocks  1711  and  1712  is computed as described above with reference to Equation (34). In decision block  1714 , the absolute value of the data storage allocation gap is greater than a data storage threshold, as described above with reference to Equation (36), control flows to block  1715 . Otherwise, control flows to block  1716 . In block  1715 , an alert is generated that indicates data storage allocation costs are not aligned with data storage allocation cost of the cloud computing industry. In block  1716 , a total resource allocation fraction is computed as described above with reference to Equation (39) based on the total resource allocation cost computed in  1507  of  FIG. 15  and the total VDC cost of the cloud computing industry. In block  1717 , a total resource allocation fraction is computed as described above with reference to Equation (41) based on the total resource allocation cost computed in  1607  of  FIG. 16  and the total VDC cost of the data center. In block  1718 , a total resource allocation gap is computed based on the total resource allocation fractions computed in blocks  1716  and  1717  as described above with reference to Equation (42). In decision block  1719 , when the absolute value of the total resource allocation gap is greater than a total resource threshold as described above with reference to Equation (44) control flows to block  1720 . In block  1720 , an alert is generate that indicates the total resource allocation cost is not aligned with total resource allocation cost of the cloud computing industry. 
         [0082]      FIG. 18  shows a control-flow diagram of the method “compute monetary impact of gaps” called in block  1406  of  FIG. 14 . In block  1801 , a monetary CPU allocation impact is computed as described above with reference to Equation (13). In block  1802 , a monetary memory allocation impact is computed as described above with reference to Equation (25). In block  1803 , a monetary data storage allocation impact is computed as described above with reference to Equation (37). In block  1804 , a monetary data resource allocation impact is computed as described above with reference to Equation (45). In blocks  1805  and  1806 , the monetary allocation impact data computed in blocks  1801 - 1804  are stored and may be displayed for viewing, such as displaying on a monitor or other display device. 
         [0083]    The methods described above with reference to  FIGS. 14-18  may be encoded in machine-readable instructions stored in one or more data-storage devices of a programmable computer, such as the computer described above with reference to  FIG. 1 . 
         [0084]    It is appreciated that the various implementations described herein are intended to enable any person skilled in the art to make or use the present disclosure. Various modifications to these implementations will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other implementations without departing from the spirit or scope of the disclosure. For example, any of a variety of different implementations can be obtained by varying any of many different design and development parameters, including programming language, underlying operating system, modular organization, control structures, data structures, and other such design and development parameters. Thus, the present disclosure is not intended to be limited to the implementations described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.