Patent Publication Number: US-7904547-B2

Title: Method, system, and program product for optimizing monitoring and discovery services for a grid computing environment

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is related in some aspects to commonly assigned U.S. application Ser. No. 10/718,419, entitled “COMPUTERIZED SYSTEM, METHOD AND PROGRAM PRODUCT FOR MANAGING AN ENTERPRISE STORAGE SYSTEM” filed Nov. 20, 2003 and herein incorporated by reference. The application is also related in some aspects to commonly assigned U.S. application Ser. No. 11/068,368 entitled “COMPUTERIZED SYSTEM, METHOD, AND PROGRAM PRODUCT FOR MANAGING APPLICATIONS IN A SHARED COMPUTER INFRASTRUCTURE” filed Feb. 28, 2005 and herein incorporated by reference. 
     FIELD OF THE INVENTION 
     In general, the present invention relates to grid computing environments. Specifically, the present invention relates to a method, system, and program product for optimizing monitoring and (resource) discovery services for a grid computing environment. 
     BACKGROUND OF THE INVENTION 
     Today&#39;s common web service architecture provides multiple services within a single infrastructure. Enterprises with common needs are also coming together to share infrastructure and business processes. An advantage of this approach is that each involved party pays only a fraction of the entire cost involved. The above-incorporated patent applications disclose a storage grid architecture that optimizes storage utilization by “gridifying” storage cells within a common environment/infrastructure. This type of structure not only provides redundancy, but also takes performance-based parameters such as storage capacity, user requirements, etc., into consideration when selecting a file sharing location. 
     A grid environment/infrastructure provides flexible, secure, coordinated resource sharing among a dynamic collection of individuals, institutions, etc. It is distinguished from conventional distributed (enterprise) computing by its focus on large-scale resource sharing, innovative applications, and, in some cases, high-performance orientation. The collection of individual resources and institutions that contribute resources to a particular grid, and/or use the resources in that grid, is referred to as a virtual organization and represents a new approach to computing and problem solving based on collaboration among multiple disciplines in computation and data-rich environments. 
     Unfortunately, none of the existing approaches addresses or resolves the conflict that exists between grid environment stability and updateability. That is, as nodes are added to the grid environment, stability is improved, while updateability becomes more difficult. Conversely, if the grid includes fewer nodes, updateability is made easier, while stability suffers. 
     In view of the foregoing, there exists a need to overcome one or more of the deficiencies in the existing art. 
     SUMMARY OF THE INVENTION 
     In general, the present invention provides a method, system, and program product for optimizing monitoring and (resource) discovery services for a grid computing environment. Specifically, under the present invention, a stability and updateability (SUP) ratio for the grid computing environment is generated. Thereafter, a size of a set (e.g., at least one) of scale free graphs for the grid computing environment is estimated. Then, based on at least one predetermined parameter such as a quantity of nodes in the grid computing environment, the set of scale free graphs is actually generated. Based on the set of scale free graphs, a set of perturbed scale free graphs is generated. As will be further described below, this is typically accomplished using a preferential rewiring technique. This process is repeated for each of the monitoring and discovery services, which will yield two sets of perturbed scale free graphs. Using these sets of graphs, a set of monitoring and discovery service (MDS) topology graphs for the grid computing environment will be generated and deployed for optimal service. 
     A first aspect of the present invention provides a method for optimizing monitoring and discovery services for a grid computing environment, comprising: generating a stability and updateability (SUP) ratio for the grid computing environment; estimating a size of a set of scale free graphs for the grid computing environment; generating the set of scale free graphs based on at least one predetermined parameter; generating a set of perturbed scale free graphs based on the set of scale free graphs; and repeating the above steps for each of the monitoring and discovery services to generate a set of monitoring and discovery service (MDS) topology graphs for the grid computing environment. 
     A second aspect of the present invention provides a system for optimizing monitoring and discovery services for a grid computing environment, comprising: a ratio estimator for generating a stability and updateability (SUP) ratio for the grid computing environment; a size estimator for estimating a size of a set of scale free graphs for the grid computing environment; a scale free graph generator for generating the set of scale free graphs based on at least one predetermined parameter; a random perturbation generator for generating a set of perturbed scale free graphs based on the set of scale free graphs; and a network generator for repeating the above steps for each of the monitoring and discovery services to generate a set of monitoring and discovery service (MDS) topology graphs for the grid computing environment. 
     A third aspect of the present invention provides a program product stored on a computer readable medium for optimizing monitoring and discovery services for a grid computing environment, the computer readable medium comprising program code for causing a computer system to perform the following steps: generating a stability and updateability (SUP) ratio for the grid computing environment; estimating a size of a set of scale free graphs for the grid computing environment; generating the set of scale free graphs based on at least one predetermined parameter; generating a set of perturbed scale free graphs based on the set of scale free graphs; and repeating the above steps for each of the monitoring and discovery services to generate a set of monitoring and discovery service (MDS) topology graphs for the grid computing environment. 
     A fourth aspect of the present invention provides a method for optimizing monitoring and discovery services for a grid computing environment, comprising: providing a computer infrastructure being operable to: generate a stability and updateability (SUP) ratio for the grid computing environment; estimate a size of a set of scale free graphs for the grid computing environment; generate the set of scale free graphs based on at least one predetermined parameter; generate a set of perturbed scale free graphs based on the set of scale free graphs; and repeat the above steps for each of the monitoring and discovery services to generate a set of monitoring and discovery service (MDS) topology graphs for the grid computing environment. 
     A fifth aspect of the present invention provides computer software embodied in a computer readable medium for optimizing monitoring and discovery services for a grid computing environment, the computer software comprising instructions for causing a computer system to perform the following steps: generating a stability and updateability (SUP) ratio for the grid computing environment; estimating a size of a set of scale free graphs for the grid computing environment; generating the set of scale free graphs based on at least one predetermined parameter; generating a set of perturbed scale free graphs based on the set of scale free graphs; and repeating the above steps for each of the monitoring and discovery services to generate a set of monitoring and discovery service (MDS) topology graphs for the grid computing environment. 
     A sixth aspect of the present invention provides a business method for optimizing monitoring and discovery services for a grid computing environment. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features of this invention will be more readily understood from the following detailed description of the various aspects of the invention taken in conjunction with the accompanying drawings in which: 
         FIG. 1  depicts a grid computing environment in accordance with the present invention. 
         FIG. 2  depicts integration of different architectures in a grid computing environment in accordance with the present invention. 
         FIG. 3  depicts the controlling of grid resources in accordance with the present invention. 
         FIG. 4  depicts the organization of grid job scheduling in accordance with the present invention. 
         FIG. 5  depicts a computerized implementation in accordance with the present invention. 
         FIG. 6  depicts a flow diagram in accordance with the present invention. 
         FIG. 7  depicts a scale free graph in accordance with the present invention. 
         FIG. 8  depicts a perturbed scale free graph in accordance with the present invention. 
     
    
    
     The drawings are not necessarily to scale. The drawings are merely schematic representations, not intended to portray specific parameters of the invention. The drawings are intended to depict only typical embodiments of the invention, and therefore should not be considered as limiting the scope of the invention. In the drawings, like numbering represents like elements. 
     BEST MODE FOR CARRYING OUT THE INVENTION 
     As indicated above, the present invention provides a method, system, and program product for optimizing monitoring and (resource) discovery services for a grid computing environment. Specifically, under the present invention, a stability and updateability (SUP) ratio for the grid computing environment is generated. Thereafter, a size of a set (e.g., at least one) of scale free graphs for the grid computing environment is estimated. Then, based on at least one predetermined parameter such as a quantity of nodes in the grid computing environment, the set of scale free graphs is actually generated. Based on the set of scale free graphs, a set of perturbed scale free graphs is generated. As will be further described below, this is typically accomplished using a preferential rewiring technique. This process is repeated for each of the monitoring and discovery services, which will yield two sets of perturbed scale free graphs. Using these sets of graphs, a set of monitoring and discovery service (MDS) topology graphs for the grid computing environment will be generated and deployed for optimization. 
     The approach of the present invention improves efficiency and stability of the MDS Index Service by providing proper and theoretically solid graphs for grid topology. The efficiency of the distributed MDS Index Service is improved, due to more uniform load distribution provided by the proposed topology graph. The stability of the MDS Index Service is improved because the proposed topology graph is stable with respect to random node failure(s). One aspect of present invention is to provide duplicate caches—one for the monitoring service, another for the resource discovery service. The network represents a graph with special statistical properties, namely: it is stable under possible topology fluctuations or node failures; and it is fast to update. 
     As used herein, the following terms have the following meanings: 
     Stability—an average distance between two nodes for a given distribution of node failures. 
     Updateability—a loss function of write and read requests related to propagating and obtaining updates. Note, that when stability level is increased, updateability will suffer. 
     Referring now to  FIG. 1 , an illustrative grid computing environment  10  is shown. In general, grid computing environment  10  allows users such as user  12  to operate a workstation  16  to store files and the like in one or more data centers or “storage cells”  14 A-B that are managed by management service  18 .  FIG. 2  illustrates the integration of different hardware architectures in a grid computing environment  10 . As shown, nodes  20 A-D, each of a different architecture, can be deployed in environment  10 . As further shown, a toolkit infrastructure  22  such as (for example) the GLOBUS TOOLKIT infrastructure, which is an open source software toolkit developed by the Globus Alliance, can be deployed in environment  10 . 
     In general, grid resources are controlled by a grid resource manager  24 , as illustrated in  FIG. 3 . For resource manager  24  to make informed decisions on resource assignments, resource manager  24  needs to know what grid resources are available, and their capacities and current utilization. This knowledge about the grid resources is maintained and provided by a Grid Information Service (GIS), also known as a Monitoring and Discovery Service (MDS). The MDS may be queried to discover the properties of the machines, computers, and networks such as the number of processors available at this moment, what bandwidth is provided, and the type of storage available. 
       FIG. 4  illustrates organization of the grid job scheduling for different machines/platforms. As can be seen, requests and responses (of resource information) flow between each of the hosts  26 A-C. As further shown, host  26 A contains an MDS client  28  that provides MDS functions. The grid&#39;s MDS is an important service of the grid. Without this service, the whole idea of virtualizing resources and load becomes unrealizable. In many cases, due to the size of the grid, the following issues arise in MDS: (1) the necessity to maintain MDS stability under possible fluctuation/failures of the grid structure; and (2) the necessity to be able to promptly distribute information about changes in resource consumption and availability. It should be noted that similar problems are common to all highly distributed systems, including peer-to-peer (P-2-P) systems. Existing MDS implementations, based on LDAP, do not provide an efficient solution for these issues. One reason is that resource monitoring requires much more frequent information update than a typical LDAP implementation can provide. Existing grids&#39; MDS are built upon an aggregation framework. The framework facilitates the building of the aggregating services (e.g., the Index, Trigger and Archive services). The Index Service, as the major service, obtains and caches information about availability and resource consumption of other services, and allows clients to query or subscribe to data. The Trigger Service subscribes to the Index Service. Upon arrival of notification, the Trigger Service checks certain preconditions. If preconditions hold, it executes some script (for example sends e-mail to an administrator). Archive Service stores historical monitoring data and enables queries against that data. 
     In any event, as will be further discussed below, the present invention yields an MDS or MDSNet topology graph based on sets of perturbed scale free graphs. Perturbed scale free graphs are generally known to be stable under uniformly distributed failures. In order to distribute the information transfer load more uniformly, the present invention provides a perturbed graph structure by randomizing a set of scale free graphs in such a way that the resulting graph provides the best information throughput for a given stability level. For a given stability and updateability (SUP) ratio, the perturbed scale free topology graph becomes optimal. As will be further described below, this procedure is repeated twice, building at least two sets of graphs, one set of graphs for the SUP ratio specific for the resource discovery service, and another set of graphs for the SUP ratio specific for the resource consumption monitoring service. The resulting topology graph will be used for the grid&#39;s MDS. 
     Referring now to  FIG. 5 , a computerized implementation  100  of the present invention is shown. As depicted, implementation  100  includes a computer system  104  deployed within a computer infrastructure  102 , which could be a grid computing environment. This is intended to demonstrate, among other things, that the present invention could be implemented within a network environment (e.g., the Internet, a wide area network (WAN), a local area network (LAN), a virtual private network (VPN), etc.), or on a stand-alone computer system. In the case of the former, communication throughout the network can occur via any combination of various types of communications links. For example, the communication links can comprise addressable connections that may utilize any combination of wired and/or wireless transmission methods. Where communications occur via the Internet, connectivity could be provided by conventional TCP/IP sockets-based protocol, and an Internet service provider could be used to establish connectivity to the Internet. Still yet, computer infrastructure  102  is intended to demonstrate that some or all of the components of implementation  100  could be deployed, managed, serviced, etc. by a service provider who offers to deploy an application in a grid environment. In any event, as mentioned above, infrastructure  102  typically represents a grid computing environment in which computer system  104  is deployed. 
     As shown, computer system  104  includes a processing unit  106 , a memory  108 , a bus  110 , and input/output (I/O) interfaces  112 . Further, computer system  104  is shown in communication with external I/O devices/resources  114  and storage system  116 . In general, processing unit  106  executes computer program code, such as service program  120 , which is stored in memory  108  and/or storage system  116 . While executing computer program code, processing unit  106  can read and/or write data to/from memory  108 , storage system  116 , and/or I/O interfaces  112 . Bus  110  provides a communication link between each of the components in computer system  104 . External devices  114  can comprise any devices (e.g., keyboard, pointing device, display, etc.) that enable a user to interact with computer system  104  and/or any devices (e.g., network card, modem, etc.) that enable computer system  104  to communicate with one or more other computing devices. 
     Computer infrastructure  102  is only illustrative of various types of computer infrastructures for implementing the invention. For example, in one embodiment, computer infrastructure  102  comprises two or more computing devices (e.g., a server cluster) that communicate over a network to perform the various process steps of the invention. Moreover, computer system  104  is only representative of various possible computer systems that can include numerous combinations of hardware. To this extent, in other embodiments, computer system  104  can comprise any specific purpose computing article of manufacture comprising hardware and/or computer program code for performing specific functions, any computing article of manufacture that comprises a combination of specific purpose and general purpose hardware/software, or the like. In each case, the program code and hardware can be created using standard programming and engineering techniques, respectively. Moreover, processing unit  106  may comprise a single processing unit, or be distributed across one or more processing units in one or more locations, e.g., on a client and server. Similarly, memory  108  and/or storage system  116  can comprise any combination of various types of data storage and/or transmission media that reside at one or more physical locations. Further, I/O interfaces  112  can comprise any system for exchanging information with one or more external devices  114 . Still further, it is understood that one or more additional components (e.g., system software, math co-processing unit, etc.) not shown in  FIG. 5  can be included in computer system  104 . However, if computer system  104  comprises a handheld device or the like, it is understood that one or more external devices  114  (e.g., a display) and/or storage system(s)  116  could be contained within computer system  104 , not externally as shown. 
     Storage system  116  can be any type of system (e.g., a database) capable of providing storage for information under the present invention such as historical data, graphs, etc. To this extent, storage system  116  could include one or more storage devices, such as a magnetic disk drive or an optical disk drive. In another embodiment, storage system  116  includes data distributed across, for example, a local area network (LAN), wide area network (WAN) or a storage area network (SAN) (not shown). 
     Shown in memory  108  of computer system  104  is service program  120 , which includes ratio estimator  122 , size estimator  124 , scale free graph generator  126 , random perturbation generator  128 , network generator  130 , and deployer  132 . The functions of these systems will be explained in conjunction with the flow diagram of  FIG. 6 . Thus, referring to  FIGS. 5 and 6  collectively, the present invention functions as follows: 
     (SUP) ratio estimator  122  typically uses historical data of resource discovery (and/or some heuristic element) and monitoring requests for the existing grid computing environment topology to generate an SUP ratio. Specifically, the SUP ratio is generated by averaging over the size of the network, possible failures and, separately, requests for updates. For similar types of conditions, these values can be extrapolated based on the size of the graph (e.g., for two types of parameters—number of nodes and number of links). Ratio estimator  122  then generates the SUP ratio using extrapolation based on these two parameters and the average level read/write load. For an illustrative example, suppose that a grid includes 1000 nodes and 4000 links, and during thirty days twenty-five failures occurred. During the same period of time 500,000 updates occurred. In such circumstances, the value will be 500000*1/1000*1/(30*60*25). This value is extrapolated for all possible values of number of links. 
     Once the SUP ratio is generated, size estimator  124  initializes itself by performing numerical experiments in order to produce a function representing the relation between parameters of the graph and the failures/updates level. Then, it reverses this function using “spline” approximation, making it a function of the failures/updates level and error tolerance with values of graph parameters (number of nodes, number of links). The inverted function is used by size estimator  124  to estimate a size of a set of scale free graphs for the grid computing environment. 
     Thereafter, scale free graph generator  126  will generate a set of scale free graphs based on at least one predetermined parameter (e.g., a quantity of nodes, a quantity of links), using any known available algorithm. 
     The following algorithm represents one illustrative possibility:
         1. For the first m o  nodes;   2. Create a pool K and add to it m o  initial nodes;   3. Create a pool L of all the other nodes;   4. Remove randomly a node  1  from the pool L and connect it to a randomly chosen node from the pool K;   5. Add node  1  to the pool K;   6. For the rest of the nodes;   7. Remove randomly a node i from the pool L;   8. Select randomly a vertex from the pool K and connect it with probability;   9. to the vertex I;       

               P   ⁡     (     k   i     )       =       k     i   ⁢                   ∑   j     ⁢     k   j               
to the vertex I;
         10. Add i to the pool K;
 
Referring to  FIG. 7 , an illustrative scale free graph  160  is shown.
       

     As an illustration of scale free graphs, a financial structure is considered. Parts of the portfolios of different companies (e.g., those quoted in a stock exchange) include shares of other companies. The subset of those traded in the same stock exchange form a network: The vertices are the companies, and an oriented link is drawn only when an organization owns more than 5% of the other. The result, shown in  FIG. 7 , clearly illustrates the presence of a scale-free network. 
     Based on the set of scale free graphs, random perturbation generator  128  ( FIG. 5 ) will utilize one of the possible perturbation schemas, for specificity below is outlined a number of perturbation schemas to generate a set of proper perturbed scale free graphs.
     S1a: Random addition. Add a new edge by connecting two random nodes.   S1b: Preferential addition. Add a new edge by connecting two unconnected nodes having the lowest degrees in the network.   S2a: Random edge rewiring. Remove a random edge, then add an edge as in S1a.   S2b: Random neighbor rewiring. Choose a random node, and then a random neighbor of that node, and remove the corresponding edge. Then add an edge as in S1a.   S3a: Preferential rewiring. Disconnect a random edge from a highest-degree node, and reconnect that edge to a random node.   S3b: Preferential random edge rewiring. Choose a random edge, disconnect it from its higher-degree node, and reconnect that edge to a random node.   

     Referring briefly to  FIG. 8 , an illustrated perturbed scale free graph  170  is depicted. Due to the choice of the parameters given above, random perturbation generator  128  ( FIG. 5 ) creates the set of perturbed scale free graphs with the following properties: (a) it is stable for “small” random failures up to the degree chosen as a parameter during generation of the set of scale free graphs (e.g., 1% or 3% or 5% or 10% or 20%), since this graph is close to a scale free graph; and (b) it is easily updateable, since preferential rewiring makes the graph much more homogeneous. 
     Regardless, the above steps will be repeated for each of the monitoring and discovery services. The goal is to generate two sets of perturbed scale free graphs (e.g., one set for each service). Using the two sets of perturbed scale free graphs, network generator  130  will generate a set of MDSNet graphs (also referred to herein as a set of MDS topology graphs). Based on the set of MDS topology graphs, network generator  130  will generate distributed caches (one for each service), as follows: each node of the graph corresponds to the cache itself, and each link in the graph corresponds to a possibility of communication between caches. 
     Once the set of MDS topology graphs are generated, deployer  132  will deploy the same. To this end, deployer  132  can use a standard grid deployment mechanism, (e.g., OGSI) to deploy the set of MDS topology graphs. Using the set of MDS topology graphs, the monitoring and discovery services provided for the grid computing environment can be optimized. 
     While shown and described herein as a method, system, and program product for optimizing monitoring and (resource) discovery services for a grid computing environment, it is understood that the invention further provides various alternative embodiments. For example, in one embodiment, the invention provides a computer-readable/useable medium that includes computer program code to enable a computer infrastructure to optimize monitoring and (resource) discovery services for a grid computing environment. To this extent, the computer-readable/useable medium includes program code that implements each of the various process steps of the invention. It is understood that the terms computer-readable medium or computer useable medium comprises one or more of any type of physical embodiment of the program code. In particular, the computer-readable/useable medium can comprise program code embodied on one or more portable storage articles of manufacture (e.g., a compact disc, a magnetic disk, a tape, etc.), on one or more data storage portions of a computing device, such as memory  108  ( FIG. 5 ) and/or storage system  116  ( FIG. 5 ) (e.g., a fixed disk, a read-only memory, a random access memory, a cache memory, etc.), and/or as a data signal (e.g., a propagated signal) traveling over a network (e.g., during a wired/wireless electronic distribution of the program code). 
     In another embodiment, the invention provides a business method that performs the process steps of the invention on a subscription, advertising, and/or fee basis. That is, a service provider, such as a Solution Integrator, could offer to optimize monitoring and (resource) discovery services for a grid computing environment. In this case, the service provider can create, maintain, support, etc., a computer infrastructure, such as computer infrastructure  102  ( FIG. 5 ) that performs the process steps of the invention for one or more customers. In return, the service provider can receive payment from the customer(s) under a subscription and/or fee agreement and/or the service provider can receive payment from the sale of advertising content to one or more third parties. 
     In still another embodiment, the invention provides a computer-implemented method for optimize monitoring and (resource) discovery services for a grid computing environment. In this case, a computer infrastructure, such as computer infrastructure  102  ( FIG. 5 ), can be provided and one or more systems for performing the process steps of the invention can be obtained (e.g., created, purchased, used, modified, etc.) and deployed to the computer infrastructure. To this extent, the deployment of a system can comprise one or more of (1) installing program code on a computing device, such as computer system  104  ( FIG. 5 ), from a computer-readable medium; (2) adding one or more computing devices to the computer infrastructure; and (3) incorporating and/or modifying one or more existing systems of the computer infrastructure to enable the computer infrastructure to perform the process steps of the invention. 
     As used herein, it is understood that the terms “program code” and “computer program code” are synonymous and mean any expression, in any language, code or notation, of a set of instructions intended to cause a computing device having an information processing capability to perform a particular function either directly or after either or both of the following: (a) conversion to another language, code or notation; and/or (b) reproduction in a different material form. To this extent, program code can be embodied as one or more of: an application/software program, component software/a library of functions, an operating system, a basic I/O system/driver for a particular computing and/or I/O device, and the like. 
     The foregoing description of various aspects of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed, and obviously, many modifications and variations are possible. Such modifications and variations that may be apparent to a person skilled in the art are intended to be included within the scope of the invention as defined by the accompanying claims.