Patent Publication Number: US-9852007-B2

Title: System management method, management computer, and non-transitory computer-readable storage medium

Description:
BACKGROUND OF THE INVENTION 
     This invention relates to a method of managing an operations system built on a computer system including a plurality of computers. 
     Along with the developments in cloud computing in recent years, the number of data centers operating computer resources as a resource pool is increasing. A provider of the data center builds an operations system for a user in the data center by allocating predetermined computer resources from the resource pool based on a user request. The user provides a predetermined service, such as a Web service, by using the operations system built in the data center. 
     In order to operate the resource pool, it is important for the provider to grasp the performance limits of the systems in the data center, and expand the scale of the data center as necessary. In general, the scale of the data center is expanded by increasing the computer resources of the data center by scaling out. 
     A method of managing a computer system is described in JP 2008-225995 A, for example. In JP 2008-225995 A, there is described “a policy creation support method for a policy creation support system used in order to control a system to be monitored so as to satisfy a contract condition, the policy creation support system being configured to support creation of a policy that contains a condition indicating a state of the system to be monitored and an action to be executed when the state of the system to be monitored satisfies the condition. The condition contains a monitoring item serving as an item to be monitored in the system to be monitored and a range of a measurement value of the monitoring item. The policy creation support system is configured to execute a holding step of holding a template for designating a type of the monitoring item required for the creation of the policy, an obtaining step of obtaining the measurement value of each resource amount of a resource to be expanded for each monitoring item corresponding to the type designated by the template, a selecting step of selecting one representative measurement value from among the measurement values for each resource amount for each monitoring item corresponding to the type designated by the template, and an output step of outputting the monitoring item, the resource amount of the monitoring item, and the range of the measurement value corresponding to the resource amount, for each monitoring item corresponding to the type designated by the template by setting a range including the selected representative measurement value as the range of the measurement value.” 
     However, the computer system and the operations system are independent of each other. Further, the configuration of the operations system is different for each user. Therefore, the performance limits of the computer system and operations system cannot be easily grasped. As a result, it is difficult to estimate the limits of scaling out, and difficult to estimate the units for increasing the computer resources. In addition, what kind of expansion to perform depends on the configuration of the computer system and operations system. 
     Further, the method used to deal with system changes is different for each data center depending on the situation, such as when the performance of the computer resources of the data center is not uniform, when clusters are formed in the data center, when the data center is built only from low-cost servers, or the like. The configuration of the operations system also suffers from the same problem. Therefore, for a data center unable to handle system changes simply by scaling out, it is difficult to estimate the performance limits of the computer system and operations system. 
     SUMMARY OF THE INVENTION 
     It is an object of this invention to estimate performance limits of a computer system and an operations system by performing an effective stress test based on the associations of each of a plurality of nodes constructing the operations system. 
     The present invention can be appreciated by the description which follows in conjunction with the following figures, wherein: a system management method for a management computer coupled to a computer system, the management computer including: a first processor; a first memory coupled to the first processor; and a first interface coupled to the first processor. The computer system includes a plurality of computers, each of the plurality of computers includes: a second processor; a second memory coupled to the second processor; and a second interface coupled to the second processor. An operations system is built on the computer system. The operations system includes a plurality of task nodes each having allocated thereto one of computer resources of one computer among the plurality of computers and computer resources of a virtual computer generated on at least one computer among the plurality of computers. The system management method includes: a first step of analyzing, by the management computer, a configuration of the computer system for specifying at least one important node, which is an important task node in the operations system; a second step of changing, by the management computer, an allocation amount of the computer resources allocated to the at least one important node for measuring a load of the operations system; a third step of calculating, by the management computer, a first weighting representing a strength of associations among the plurality of task nodes based on a measurement result of the load; and a fourth step of specifying, by the management computer, a range impacted by a change in the load of the at least one important node based on the calculated first weighting. 
     According to one embodiment of this invention, the load on the operations system may be measured by focusing on important nodes specified based on the configuration of the computer system from which the operations system is built. Further, the impact range of the important nodes may be specified based on the strength of the associations among the task nodes. As a result, the performance limit of the operations system may be estimated. 
     Objects, configurations, and effects other than those described above become apparent from the following descriptions of embodiments of this invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention can be appreciated by the description which follows in conjunction with the following figures, wherein: 
         FIG. 1  is a block diagram for illustrating an outline of an embodiment of this invention; 
         FIG. 2  is an explanatory diagram for illustrating an example of a configuration of a overall system according to a first embodiment of this invention; 
         FIG. 3  is a block diagram for illustrating a configuration example of a management server of the first embodiment; 
         FIG. 4  is a block diagram for illustrating a configuration example of blade servers of the first embodiment; 
         FIG. 5  is an explanatory diagram for illustrating an example of a topology management table of the first embodiment; 
         FIG. 6  is an explanatory diagram for illustrating an example of a logical configuration management table of the first embodiment; 
         FIG. 7  is an explanatory diagram for illustrating an example of a task management table of the first embodiment; 
         FIG. 8  is an explanatory diagram for illustrating an example of a node management table of the first embodiment; 
         FIG. 9  is an explanatory diagram for illustrating an example of a threshold management table of the first embodiment; 
         FIG. 10  is an explanatory diagram for illustrating an example of an adjustment method management table of the first embodiment; 
         FIG. 11  is an explanatory diagram for illustrating an example of a system performance table of the first embodiment; 
         FIG. 12  is an explanatory diagram for showing an example of a weighting management table of the first embodiment; 
         FIG. 13  is an explanatory diagram for showing an example of a chart of the weighting management table of the first embodiment in matrix form; 
         FIG. 14  is an explanatory diagram for illustrating an example of a rule management table of the first embodiment; 
         FIG. 15  is a flowchart for illustrating an outline of processing executed by the management server of the first embodiment; 
         FIG. 16  is a flowchart for illustrating an example of node selection processing of the first embodiment; 
         FIG. 17  is a flowchart for illustrating an example of measurement processing of the first embodiment; 
         FIG. 18  is an explanatory diagram for illustrating an example of a measurement result of the first embodiment; 
         FIG. 19  is a flowchart for illustrating an example of counting processing of the first embodiment; 
         FIG. 20  is a flowchart for illustrating an example of monitoring processing of the first embodiment; 
         FIG. 21  is an explanatory diagram for illustrating an example of an orientation parameter registration screen of a second embodiment; 
         FIG. 22  is a flowchart for illustrating an example of processing for generating a superposition matrix of a fourth embodiment; 
         FIG. 23  is a block diagram for illustrating a configuration example of the management server of a fifth embodiment; and 
         FIG. 24  is a flowchart for illustrating an example of meta tag generation processing of the fifth embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       FIG. 1  is a block diagram for illustrating an outline of an embodiment of this invention. 
     In  FIG. 1 , an operations system configured to execute predetermined tasks and a management server  100  configured to manage the operations system are coupled to each other via a management network. Further, the operations system is coupled to an external network via a task network. 
     The operations system illustrated in  FIG. 1  includes one database server  10 , two application servers  11 , four Web servers  12 , and one load balancer  13 . In the following description, the elements, such as the database server, constructing the operations system are also referred to as task nodes. 
     The operations system is built on a computer system providing computer resources. The computer system includes a physical layer built from physical computers, physical switches, and the like, and a logical layer built from virtual computers realized using physical computers, virtual switches realized using physical switches, and the like. In the following description, the physical computers and the like constructing the physical layer are also referred to as physical nodes, and the virtual computers and the like constructing the logical layer are also referred to as logical nodes. 
     The task nodes are realized using the physical nodes and the logical nodes. For example, in realizing the database server  10 , the database server may be realized using a blade server  223 , which is a physical node, or the database server may also be realized using a virtual computer, namely, a logical node, created on the blade server  223 . 
     This embodiment is directed to estimating a performance limit of the operations system. In order to estimate the performance limit, task nodes having strict performance requirements in the operations system are specified, and a stress test is carried out on the specified task nodes based on a sequence such as that described below. 
     (1) First, the management server  100  specifies important task nodes in the operations system based on the configuration of the operations system, and the configuration of the computer system from which the operations system is built. In the following description, the important task nodes are also referred to as important nodes.
 
(2) The management server  100  carries out a stress test on the important nodes, and measures the impact on the overall operations system.
 
(3) Based on the measurement result of the stress test, the management server  100  calculates a minimum cut set (impact range), which is a combination of nodes having a large impact on the operations system. It should be noted that the minimum cut set includes one or more important nodes. Further, the management server  100  estimates the size of the impact on the task nodes included in the impact range caused by changes in the load of the important nodes.
 
     Based on the processing such as that described above, the performance limit (performance characteristic) of the operations system can be estimated. 
     In the example illustrated in  FIG. 1 , the measurement result of a stress test carried out when the amount of allocated resources of the database server specified as an important node has been reduced by 30% is shown. In this case, a minimum cut set such as that indicated by the dotted line is calculated. In other words, in the operations system, the task nodes that are impacted by the load of the database server, which is an important node, can be specified. 
     Further, in this embodiment, the management server  100  is configured to execute monitoring processing based on the measurement result of a stress test carried out during operation of the operations system. In this case, the management server  100  displays a meter  15  representing an estimated value of the load of the task nodes included in the impact range, a critical value thereof, or the like. As a result, an estimation can be made regarding whether or not expansion of the computer system and the operations system is necessary. 
     As a result, an alert can be issued to the provider or to the user before the load of the operations system reaches a certain level or more, which allows countermeasures to be drawn up in advance. 
     First Embodiment 
       FIG. 2  is an explanatory diagram for illustrating an example of a configuration of the overall system according to a first embodiment of this invention. 
     The system according to the first embodiment includes a management server  100  and a computer system from which the operations system is built. The computer system, which is a system providing computer resources, includes a plurality of server apparatus  220 , a storage subsystem  260 , an NW-SW  250 , and an FC-SW  140 . 
     The management server  100  is configured to manage the computer system. The management server  100  is coupled to a management interface (management I/F)  211  of an NW-SW  210  (management network switch)  210  and a management interface  251  of an NW-SW (task network switch)  250  via the NW-SW  210 . The management server  100  can set a virtual local area network (VLAN) to both the NW-SW  210  and the NW-SW  250 . 
     The NW-SW  210  forms a management network. The management network is a network that allows the management server  100  to distribute an operating system (OS) and applications running on the plurality of server apparatus  220 , and to operate and manage power control and the like. 
     The NW-SW  250  forms a task network. The task network is a network used by the applications to be executed by the server apparatus  220  or a virtual machine (VM) on the server apparatus  220 . It should be noted that the NW-SW  250  is configured to communicate with an external client computer coupled via a wide area network (WAN) or the like. 
     In the following description, physical computer resources, such as the server apparatus  220 , the FC-SW  140 , the NW-SW  250 , and the storage subsystem  260 , are referred to as physical nodes, and virtual computer resources, such as the VM and the virtual switches, are referred to as logical nodes. 
     The management server  100  is coupled to a management interface (management I/F)  161  of the storage subsystem  260  via the fiber channel switch (FC-SW)  140 . The management server  100  is configured to manage logical units (LUs)  262  in the storage subsystem  260 . In the example illustrated in  FIG. 1 , the management server  100  manages N-number of logical units LU  1  to LU-n. 
     The management server  100 , which includes a control module  201 , stores a management table group  202 . The management table group  202  contains information on the configuration of the computer system, for example. The control module  201  is configured to execute a stress test (black box test) on the operations system built on the computer system by referring to the information contained in the management table group  202 , and update the information contained in the management table group  202  based on the measurement result of the stress test. Details of the configuration of the management server  100  are described later with reference to  FIG. 3 . 
     Each server apparatus  220  provides computer resources to be allocated to the task nodes in a manner described later. The server apparatus  220  are coupled to the NW-SW  210  and the NW-SW  250  via an input/output (I/O) device or the like. 
     Each server apparatus  220  has a service processor  221  and a plurality of blade servers  223  mounted thereon. The service processor  221  is configured to monitor the blade servers  223  mounted on the server apparatus  220 . Each server apparatus  220  is coupled to the management server  100  via the service processor  221 . The blade servers  223  have an OS and applications running thereon. The software configuration and the hardware configuration of the blade servers  223  are described later with reference to  FIG. 4 . 
     It should be noted that one or more VMs may be created on the blade servers  223 , and one or more virtual switches may be created on the FC-SW and the NW-SW  250 . A virtual switch may also be created on the blade servers  223 . 
     It should be noted that although the server apparatus  220  includes a blade server  223 , this embodiment is not limited to this. For example, a common computer including a processor, a memory, a network interface, and the like may be used as the server apparatus  220 . 
     The storage subsystem  260  provides a storage area to be used by the OS and the like running on the server apparatus  220 . The storage subsystem  260  includes a controller (not shown), a plurality of storage media (not shown), a disk interface (not shown), and a network interface (not shown). Examples of the storage media include a hard disk drive (HDD) and a solid state drive (SSD). 
     The controller (not shown), which forms a redundant array of independent disks (RAID) system using a plurality of storage media, is configured to create a plurality of LUs  162  from RAID volumes. The storage subsystem  260  provides the LUs  162  as storage areas to be used by the OS and the like. 
       FIG. 3  is a block diagram for illustrating a configuration example of the management server  100  of the first embodiment. 
     The management server  100  includes a processor  301 , a memory  302 , a disk interface  303 , and a network interface  304 . 
     The processor  301  is configured to execute programs stored in the memory  302 . The memory  302  stores programs to be executed by the processor  301  and information required to execute those programs. The programs and information stored in the memory  302  are described later. 
     The disk interface  303  is an interface for accessing the storage subsystem  260 . The network interface  304  is an interface for communicating to/from another apparatus via an Internet protocol (IP) network. 
     It should be noted that, although not shown in  FIG. 3 , the management server  100  may also include a basement management controller (BMC) configured to control the power supply and each of the interfaces. 
     A program for realizing the control module  201  and the management table group  202  are stored in the memory  302 . The control module  201  includes a plurality of program modules. Specifically, the control module  201  includes a weighting calculation module  310 , a measurement module  311 , an estimation module  312 , and an operations system monitoring module  313 . 
     The processor  301  is configured to operate as a function module for realizing a predetermined function by operating based on program modules for realizing the weighting calculation module  310 , the measurement module  311 , the estimation module  312 , and the operations system monitoring module  313 . For example, by operating based on the program module for realizing the weighting calculation module  310 , the processor  301  functions as the weighting calculation module  310 . This is the same for the other programs as well. 
     The weighting calculation module  310  is configured to calculate a weighting for evaluating an importance level of each of a plurality of task nodes in the operations system. The weighting calculation module  310  of the first embodiment calculates the weighting of the task nodes based on the configuration of the physical computer resources (physical nodes) and the configuration of the logical computer resources (logical nodes). Details of the processing executed by the weighting calculation module  310  are described later with reference to  FIG. 16 . 
     The measurement module  311  is configured to measure the impact on the operations system by executing the predetermined stress test (black box test) on the task nodes. Details of the processing executed by the measurement module  311  are described later with reference to  FIG. 17 . 
     The estimation module  312  is configured to generate information for estimating the performance limit of the operations system based on a measurement result. Details of the processing executed by the estimation module  312  are described later with reference to  FIG. 19 . 
     The operations system monitoring module  313  is configured to monitor the operations system based on the information on the performance limit of the operations system, and display a monitoring result. Further, the operations system monitoring module  313  may also display processing results of the weighting calculation module  310 , the measurement module  311 , and the estimation module  312 . Details of the processing executed by the operations system monitoring module  313  are described later with reference to  FIG. 20 . 
     The management table group  202  stores various kinds of information for managing the computer system and the operations system. Specifically, the management table group  202  stores a topology management table  320 , a logical configuration management table  321 , a task management table  322 , a node management table  323 , a threshold management table  324 , an adjustment method management table  325 , a system performance table  326 , a weighting management table  327 , and a rule management table  328 . 
     It should be noted that there is one node management table  323 , one system performance table  326 , and one weighting management table  327  for each operations system. 
     The topology management table  320  stores information on the physical nodes. Details of the topology management table  320  are described later with reference to  FIG. 5 . The logical configuration management table  321  stores information on the logical nodes. Details of the logical configuration management table  321  are described later with reference to  FIG. 6 . The task management table  322  stores information on the programs to be executed by the operations system. Details of the task management table  322  are described later with reference to  FIG. 7 . The node management table  323  stores information on the task nodes. Details of the node management table  323  are described later with reference to  FIG. 8 . 
     The threshold management table  324  stores a threshold to be used for selecting the important nodes based on the weighting of the task nodes. Details of the threshold management table  324  are described later with reference to  FIG. 9 . The adjustment method management table  325  stores stress test policies. Details of the adjustment method management table  325  are described later with reference to  FIG. 10 . 
     The system performance table  326  stores stress test measurement results. Details of the system performance table  326  are described later with reference to  FIG. 11 . The weighting management table  327  stores information on the importance level of the task nodes and associations among the task nodes in the operations system. Details of the weighting management table  327  are described later with reference to  FIG. 12 . 
     The rule management table  328  stores methods of changing the configuration of the operations system or computer system, and the change content. Details of the rule management table  328  are described later with reference to  FIG. 14 . 
     The programs for realizing the control module  201  and each of the tables in the management table group  202  may be stored in the storage subsystem  260 , a storage device such as a non-volatile semiconductor memory, a hard disk drive, or a SSD, or a non-transitory computer-readable data storage medium such as an integrated circuit (IC) card, a secure digital (SD) card, or a digital versatile disc (DVD). 
     It should be noted that the type of server for the management server  100  may be any of a physical server, a blade server, a virtual server, a logically-partitioned or physically-partitioned server, and the like. The effects of this embodiment can be obtained regardless of the type of server that is used. 
       FIG. 4  is a block diagram for illustrating a configuration example of the blade servers  223  of the first embodiment. 
     Each blade server  223  includes a processor  401 , a memory  402 , a network interface  403 , a disk interface  404 , a BMC  405 , and a peripheral component interconnect (PCI) Express interface  406 . 
     The processor  401  is configured to execute programs stored in the memory  402 . The memory  402  stores programs to be executed by the processor  401  and information required to execute those programs. The programs and information stored in the memory  302  are described later. 
     The network interface  403  is an interface for communicating to/from another apparatus via an IP network. The disk interface  404  is an interface for accessing the storage subsystem  260 . 
     The BMC  405  controls the power supply and each of the interfaces. The PCI-Express interface  406  is an interface for coupling to a PCIex-SW. 
     An OS  411  and programs for realizing an application  421  and a monitoring module  422  are stored in the memory  402 . The processor  401  manages the devices in the blade server  223  by executing the OS  411  on the memory  402 . The application  421  providing the task and the monitoring module  422  operate under the control of the OS  411 . 
     It should be noted that, as described below, the memory  402  may also store a program for realizing a virtualization module configured to manage a virtual computer. 
     It should also be noted that in the example illustrated in  FIG. 4 , one network interface  403 , one disk interface  404 , and one PCI-Express interface  406  are shown. However, a plurality of each of those interfaces may be arranged. For example, each blade server  223  may include a network interface coupling to the NW-SW  210  and a network interface coupling to the NW-SW  250 . 
       FIG. 5  is an explanatory diagram for illustrating an example of the topology management table  320  of the first embodiment. 
     The topology management table  320  stores information on the physical configuration of the computer system from which the operations system is built. Specifically, the topology management table  320  includes an identifier  501 , a UUID  502 , a physical node identifier  503 , a device name  504 , a property  505 , a coupling destination device name  506 , a reliability type  507 , and a eigenvalue  508 . 
     The identifier  501  is an identifier for uniquely identifying entries in the topology management table  320 . The UUID  502  stores a universal unique identifier (UUID), which is an identifier having a format that is defined so as not to be duplicated. The physical node identifier  503  is an identifier for uniquely identifying the physical nodes constructing the physical layer. In this embodiment, an apparatus identifier is used as the identifier of the physical nodes. 
     The device name  504  is an identifier for uniquely identifying the devices the physical nodes have. In the case of an entry representing a physical node itself, the device name  504  is left blank. 
     The property  505  is information representing the performance of a physical node itself, or, information representing the performance of the device corresponding to the device name  504 . When a port name of the FC-SW  240  is stored in the device name  504 , information on a vender type, a RAID configuration, a virtualization type, a support function, a firmware version, and the like is stored in the property  505 . 
     The coupling destination device name  506  stores information on another device coupled to the device corresponding to the device name  504 . It should be noted that when the device name  504  is blank, that is, for an entry corresponding to a physical node itself, the identifier of the another physical node connected to the physical node is stored in the coupling destination device name  506 . The reliability type  507  is information on the configuration, such as redundancy, of the physical node or of the device corresponding to the coupling destination device name  506 . 
     The eigen value  508  is a value for evaluating the importance level of the physical node or device corresponding to the entry. In other words, the eigen value  508  is a value for evaluating the importance level of the configuration of the physical layer in the computer system. In this case, the configuration of the physical layer includes the hardware configuration and software configuration of the physical nodes, and the connection configuration among the plurality of physical nodes. The eigen value  508  may be set in advance, or may be set by the administrator or the like operating the management server  100 . 
       FIG. 6  is an explanatory diagram for illustrating an example of the logical configuration management table  321  of the first embodiment. 
     The logical configuration management table  321  stores information on characteristics of the nodes in the operations system. Specifically, the logical configuration management table  321  includes an identifier  601 , a UUID  602 , a logical node identifier  603 , a type  604 , an adjustment metric  605 , an adjustment parameter  606 , a physical node identifier  607 , a system configuration  608 , a cascade connection  609 , and a eigen value  610 . 
     The identifier  601  is an identifier for uniquely identifying entries in the logical configuration management table  321 . The logical node identifier  603  is an identifier for uniquely identifying the logical nodes constructing the logical layer. In this embodiment, the identifier of the VM itself and the identifier of the virtual switch itself are used as identifiers of the logical nodes. The type  604  is information representing the type of performance changing method available to the logical nodes. 
     The adjustment metric  605  is an item measured in order to grasp the load of the logical nodes in the black box test. For example, “CPU”, “I/O”, and “memory” are stored in the adjustment metric  605  for the entry indicated by “1” in the identifier  601 , and hence processor usage, I/O count, memory usage, or the like of logical node 1 is measured. The adjustment parameter  606  is a parameter for performing an adjustment when changing the load on the logical nodes in the black box test. 
     The physical node identifier  607  is an identifier of a physical node allocating computer resources to a logical node. The physical node identifier  607  is the same as the physical node identifier  503 . 
     The system configuration  608  is information on the configuration of the logical nodes in the logical layer. In the example shown in  FIG. 6 , “scale out type”, “scale up type”, and “mesh type” are stored in the system configuration  608  as information representing the configuration of the system. For example, “scale out type” represents a configuration in which logical nodes of the same type perform processing in parallel. Further, a eigen value for the configuration of the logical nodes in the logical layer is set in the system configuration  608 . For example, “1.0” is set as the eigen value in the system configuration  608  for the entry indicated by “1” in the identifier  601 . In addition, the system configuration  608  stores an identifier of the logical nodes forming a cluster, or, an identifier of logical nodes connected in parallel, for example. 
     The cascade connection  609  is an identifier of a logical node connected to a logical node corresponding to the logical node identifier  603  in a series configuration. An example of the series configuration is a Web three-tier model. The eigen value  610  is a value for evaluating the importance level of the logical node corresponding to the logical node identifier  603 . In other words, the eigen value  610  is a value for evaluating the importance level of the configuration of the logical layer in the computer system. In this case, the configuration of the logical layer includes the hardware configuration and software configuration of the logical nodes, and the connection configuration among the plurality of logical nodes. The eigen value  610  may be set in advance, or may be set by the administrator or the like operating the management server  100 . 
       FIG. 7  is an explanatory diagram for illustrating an example of the task management table  322  of the first embodiment. 
     The task management table  322  stores information on the tasks to be executed by the operations system. Specifically, the task management table  322  stores a task identifier  701 , a UUID  702 , a task software name  703 , task setting information  704 , a priority order  705 , and a eigen value  706 . 
     The task identifier  701  is an identifier for uniquely identifying the tasks in the operations system. The task software name  703  is a name of the software to be executed in order to provide a task. The task setting information  704  is setting information required in order to execute the task corresponding to the task identifier  701 . The priority order  705  is a priority order of the tasks in the operations system. A smaller value for the priority order  705  indicates a more important task. Further, the priority order  705  also stores information on the configuration required for the task. The eigen value  706  is a value for evaluating the importance level of the task. 
       FIG. 8  is an explanatory diagram for illustrating an example of the node management table  323  of the first embodiment. 
     The node management table  323  stores information on the task nodes constructing the operations system. Specifically, the node management table  323  stores a task node identifier  801 , a UUID  802 , an assigned node identifier  803 , a task type  804 , a task identifier  805 , a connected node identifier  806 , and associated information  807 . 
     The task node identifier  801  is an identifier for uniquely identifying task nodes in the operations system. 
     The assigned node identifier  803  is an identifier of a node providing computer resources to a task node. When a physical node provides computer resources to a task node, the identifier of the physical node is stored in the assigned node identifier  803 . When a logical node provides computer resources to a task node, the identifier of the logical node is stored in the assigned node identifier  803 . 
     The task type  804  is the type of task to be executed by the task node. The task identifier  805  is an identifier of the task corresponding to the task type  804 . The task identifier  805  is the same as the task identifier  701 . The connected node identifier  806  is an identifier of another task node connected to the task node corresponding to the task node identifier  801 . The associated information  807  is information on the configuration required for the task node. 
     As shown in  FIG. 8 , a eigen value is not set for the task nodes. The reason for this is because even for identical operations systems, if the configuration of the computer systems from which those operations systems are built is different, or if the content of the tasks to be executed by the operations systems is different, the associations among the task nodes and the performance limits of the task nodes are also different. 
     Therefore, in this embodiment, the management server  100  is configured to calculate a weighting representing an importance level of the task nodes based on the configuration of the physical layer, the configuration of the logical layer, and the content of the tasks. The management server  100  is configured to specify the important nodes of the operations system based on the calculated weighting of the task nodes. 
     The important nodes have a high likelihood of having a large impact on the operations system. Therefore, measurement can be efficiently carried out by executing a stress test on the important nodes. Further, the performance limit of the operations system can also be estimated by grasping the behavior of the overall operations system with respect to the load of the important nodes. It should be noted that the stress test may be executed on task nodes other than the important nodes. For example, the stress test may be carried out on the task nodes connected to the important nodes. 
     This embodiment improves calculation efficiency and increases stress test accuracy by executing the stress test only on the important nodes. 
     In this embodiment, there is one node management table  323  for each operations system. An identifier of the operations system is associated with the node management table  323 . 
       FIG. 9  is an explanatory diagram for illustrating an example of the threshold management table  324  of the first embodiment. 
     The threshold management table  324  stores a threshold to be used when the management server  100  specifies the important nodes. Specifically, the threshold management table  324  includes an identifier  901 , a task identifier  902 , and a threshold  903 . 
     The identifier  901  is an identifier for uniquely identifying entries in the threshold management table  324 . The task identifier  902  is an identifier for uniquely identifying tasks in the operations system. The task identifier  902  is the same as the task identifier  701 . The threshold  903  is a threshold for a task. 
     In this embodiment, a threshold is set for each task. However, a single threshold may be set for the whole operations system. 
       FIG. 10  is an explanatory diagram for illustrating an example of the adjustment method management table  325  of the first embodiment. 
     The adjustment method management table  325  stores information on a method of adjusting the load on the physical nodes or the logical nodes in the stress test. Specifically, the adjustment method management table  325  includes an identifier  1001 , a node type  1002 , and an adjustment method  1003 . 
     The identifier  1001  is an identifier for uniquely identifying entries in the adjustment method management table  325 . The node type  1002  is the type of the node to be adjusted. In the node type  1002 , “physical server”, “SW”, “VM”, “vSW”, and the like are stored. 
     The adjustment method  1003  includes information on the method of adjusting the load on the physical nodes or the logical nodes corresponding to the node type  1002 . Specifically, the adjustment method  1003  includes an adjustment parameter type  1004 , an adjustment value  1005 , and a priority  1006 . 
     The adjustment parameter type  1004  is the type of parameter (adjustment parameter) to be adjusted at the node corresponding to the node type  1002 . The adjustment value  1005  is an adjustment value of the adjustment parameter. The priority  1006  is a priority order of the adjustment method. More specifically, in the priority  1006 , a value is stored for determining the adjustment method to be employed when there are a plurality of entries having the same node type  1002  and adjustment parameter type  1004 , but a different adjustment value  1005 . In this embodiment, a smaller value indicates a higher priority order. 
     The content of the adjustment method is now described. 
     The entry indicated by “1” in the identifier  1001  has “VM” for the node type  1002 , “CPU” for the adjustment parameter type  1004 , and “−10%” for the adjustment value  1005 . In this case, the management server  100  decreases the allocation ratio of the virtual processor allocated to the VM by “10%”, and measures the impact on the overall operations system. The management server  100  subsequently decreases the allocation ratio of the virtual processor by “10%” step by step, and measures the impact on the overall operations system. 
     Further, the entry indicated by “5” in the identifier  1001  has “VM” for the node type  1002 , “VM” for the adjustment parameter type  1004 , and “−1” for the adjustment value  1005 . In this case, the management server  100  decreases the number of VMs by “1”, and measures the impact on the overall operations system. The management server  100  subsequently decreases the number of VMs by “1” step by step, and measures the impact on the overall operations system. 
     In addition, the entry indicated by “6” in the identifier  1001  has “VM” for the node type  1002 , “CPU” for the adjustment parameter type  1004 , and “vCPU−1 with reboot” for the adjustment value  1005 . In this case, the management server  100  decreases the number of virtual processors for the VM by “1”, reboots the system, and then measures the impact on the overall operations system. The management server  100  subsequently decreases the number of virtual processors by “1” step by step, reboots the system, and then measures the impact on the overall operations system. 
     It should be noted that when the load on the operations system changes suddenly, adjustment may be carried out in even more incremental steps. 
       FIG. 11  is an explanatory diagram for illustrating an example of the system performance table  326  of the first embodiment. 
     The system performance table  326  stores information on a performance characteristic function generated based on the measurement result of the stress test. Specifically, the system performance table  326  includes an identifier  1101 , an important node identifier  1102 , an associated node identifier  1103 , and a performance characteristic function  1104 . 
     The identifier  1101  is an identifier for uniquely identifying entries in the system performance table  326 . In the system performance table  326 , there is one entry for each important node. The important node identifier  1102  is an identifier of the task nodes that are important nodes. The associated node identifier  1103  is an identifier of the task nodes that are impacted by the important nodes. One or more associated nodes exist for each important node. In the following description, the task nodes impacted by the important nodes are also referred to as associated nodes. 
     The performance characteristic function  1104  stores information on a performance characteristic function representing a relationship between the important nodes and the associated nodes. Specifically, the performance characteristic function  1104  includes an adjustment parameter type  1105  and a function  1106 . 
     The adjustment parameter type  1105  is an adjustment parameter of the important nodes, and corresponds to a variable of the performance characteristic function. The function  1106  stores the performance characteristic function. In this embodiment, the performance characteristic function is calculated as a change in the load of an associated node based on the adjustment parameter. The load of the associated node is the processor usage. It should be noted that memory usage, network usage, response time, and the like may also be used as the load of the associated node. 
     In this case, “X” stored in the function  1106  represents the value of the adjustment parameter corresponding to the adjustment parameter type  1105 , and “Y” represents the load of the associated node. 
     In this embodiment, there is one system performance table  326  for each operations system. An identifier of the operations system is associated with the system performance table  326 . 
       FIG. 12  is an explanatory diagram for showing an example of the weighting management table  327  of the first embodiment.  FIG. 13  is an explanatory diagram for showing an example of a chart of the weighting management table  327  of the first embodiment in matrix form. 
     The weighting management table  327  stores information on the strength of the associations among the task nodes, which is calculated based on the measurement result of the stress test. Specifically, the weighting management table  327  includes an identifier  1201 , a type  1202 , a parameter type  1203 , a task node identifier  1204 , and a weighting  1205 . 
     The identifier  1201  is an identifier for uniquely identifying entries in the weighting management table  327 . The type  1202  is the type of object for which a weighting is to be calculated. In the case of an entry corresponding to the weighting of a node, “node” is stored in the type  1202 . In the case of an entry corresponding to the weighting of an edge between two nodes, “edge” is stored in the type  1202 . The parameter type  1203  stores the type of parameter for which one task node has an impact on another task node. 
     The task node identifier  1204  is an identifier of a task node associated with the type  1202 . For example, when “node” is stored in the type  1202 , the identifier of one task node is stored in the task node identifier  1204 , and when “edge” is stored in the type  1202 , the identifiers of the two task nodes connected by the edge are stored in the task node identifier  1204 . 
     The weighting  1205  is the weighting of a task node or an edge. In this case, the weighting of a task node is the value for evaluating the importance level of the task node in the operations system. The weighting of an edge is the value for evaluating the strength of the association between the task nodes. 
     The weighting management table  327  shown in  FIG. 12  can be represented as data in matrix form, such as that shown in  FIG. 13 . In this case, the diagonal components of the matrix correspond to the weighting of the nodes, and the off-diagonal components correspond to the weighting of the edges. For example, matrix component A11 corresponds to the weighting of the task node 1, and matrix component A12 corresponds to the weighting of the edge connecting the task node 1 and the task node 2. 
     There is a possibility that some matrix element entries are not present in the weighting management table  327 . In this embodiment, when an entry corresponding to a matrix element is not present, the value for that matrix element is taken to be “0”. 
     In this embodiment, there is one weighting management table  327  for each operations system. An identifier of the operations system is associated with the weighting management table  327 . 
       FIG. 14  is an explanatory diagram for illustrating an example of the rule management table  328  of the first embodiment. 
     The rule management table  328  stores a method of changing the configuration of the operations system or computer system, and the change content. Specifically, the rule management table  328  includes a task identifier  1401 , a UUID  1402 , a task type  1403 , associated information  1404 , a priority order  1405 , and a rule  1406 . 
     The task identifier  1401  is an identifier for uniquely identifying tasks in the operations system. The task identifier  1401  is the same as the task identifier  701 . The task type  1403  is the type of task to be executed by the task node. The task type  1403  is the same as the task type  804 . The associated information  1404  is information on the configuration required for the task corresponding to the task type. 
     The priority order  1405  is a priority order of the tasks in the operations system. In this embodiment, a smaller value for the priority order  1405  indicates a higher priority order. The rule  1406  is the change content of the specific configuration of the operations system. In this embodiment, one or more pieces of change content are stored in the rule  1406 . In this case, all of the pieces of change content may be applied, or the change content may be applied up to the point when performance of the operations system has improved. 
       FIG. 15  is a flowchart for illustrating an outline of processing executed by the management server  100  of the first embodiment. 
     The control module  201  of the management server  100  starts the processing when an instruction to execute a stress test is received from the user or the like. It should be noted that the trigger for the management server  100  to start the processing is not limited to this. For example, the management server  100  may execute the processing periodically, or may start the processing when a change to the operations system or computer system is detected. The identifier of the operations system on which processing is to be executed is input to the management server  100 . 
     First, the control module  201  executes node selection processing (Step S 100 ). In the node selection processing, the control module  201  analyzes the configuration of the computer system, and selects one or more important nodes from among the plurality of task nodes constructing the operations system based on the analysis result. Details of the node selection processing are described later with reference to  FIG. 16 . 
     The control module  201  then executes measurement processing (Step S 101 ), and counting processing based on the result of the measurement processing (Step S 102 ). In the measurement processing, a stress test focused on the important nodes selected in the node selection processing is executed. In the counting processing, based on the result of the measurement processing, information representing an association between two task nodes is generated. Details of the measurement processing are described later with reference to  FIG. 17 . Further, details of the counting processing are described later with reference to  FIG. 19 . 
     The processing from Step S 100  to Step S 102  is executed before operation of the operations system. After operation of the operations system has started, the control module  201  executes monitoring processing on the operations system based on the results of the measurement processing and the counting processing (Step S 103 ). Details of the monitoring processing are described later with reference to  FIG. 20 . 
       FIG. 16  is a flowchart for illustrating an example of the node selection processing of the first embodiment. The weighting calculation module  310  included in the control module  201  executes the node selection processing. 
     The weighting calculation module  310  starts loop processing of the task nodes (Step S 200 ). At this stage, the weighting calculation module  310  selects a task node to be processed from among the task nodes included in the operations system. 
     Specifically, the weighting calculation module  310  selects one entry by referring to the node management table  323  corresponding to the input identifier of the operations system. In this embodiment, the weighting calculation module  310  makes the selection in order of the entries from the top of the node management table  323 . It should be noted that the priority order may be set in advance based on the task type or task content, and the weighting calculation module  310  may be configured to select the entry based on that priority order. 
     The weighting calculation module  310  obtains the eigen values associated with the selected task node from the topology management table  320  and the logical configuration management table  321  (Step S 201 ). Specifically, processing such as the following is executed. 
     The weighting calculation module  310  refers to the assigned node identifier  803  of the entry selected from the node management table  323 , and specifies a node providing computer resources to the selected task node. 
     When the node providing computer resources to the task node is a physical node, the weighting calculation module  310  refers to the topology management table  320 , and retrieves all of the entries having a physical node identifier  503  that matches the assigned node identifier  803 . The weighting calculation module  310  obtains the value of the eigen value  508  of all of the retrieved entries. 
     When the node providing computer resources to the task node is a logical node, the weighting calculation module  310  refers to the logical configuration management table  321 , and retrieves the entries having a logical node identifier  603  that matches the assigned node identifier  803 . The weighting calculation module  310  obtains the value included in the system configuration  608  and the value of the eigen value  610  of the retrieved entries. 
     In addition, the weighting calculation module  310  refers to the topology management table  320 , and retrieves all of the entries having a physical node identifier  503  that matches the physical node identifier  607  of the entries retrieved from the logical configuration management table  321 . The weighting calculation module  310  obtains the value of the eigen value  508  of all of the retrieved entries. 
     It should be noted that in this embodiment, the physical nodes such as the blade server  223  are handled as task nodes, but the physical nodes such as the NW-SW  250  are not handled as task nodes. In the task layer, the physical nodes such as the NW-SW  250  are handled as edges. As a result, when the weighting calculation module  310  calculates the eigen values, it is necessary to set in advance whether or not consideration needs to be given to the physical nodes such as the NW-SW  250 . In this embodiment, the physical nodes that are taken into consideration when calculating the eigen values for the task nodes are set in advance. It should be noted that the eigen values of the physical nodes included in the edges may also be used as a weighting coefficient of the edges. 
     The above is a description of the processing carried out in Step S 201 . 
     Next, the weighting calculation module  310  calculates the weighting of each task node by using the eigen values obtained from the topology management table  320  and the logical configuration management table  321  (Step S 202 ). Various methods of calculating the weighting of each task node may be employed. For example, a method such as the following calculation method may be employed. 
     When the node providing computer resources to the task node is a physical node, the weighting calculation module  310  calculates a first total value by adding together all of the values obtained from the eigen values  508  of the topology management table  320 . This first total value is taken as the weighting of the task node. 
     When the node providing computer resources to the task node is a logical node, the weighting calculation module  310  calculates a first total value by adding together all of the values obtained from the eigen value  508  of the topology management table  320 . Further, the weighting calculation module  310  calculates a second total value by adding together the values included in the system configuration  608  of the logical configuration management table  321  and the values of the eigen values  610 . In addition, the weighting calculation module  310  calculates a third total value by adding together the first total value and the second total value. This third total value is taken as the weighting of the task node. 
     Further, a method using the second total value as the weighting coefficient may be employed. In this case, the weighting calculation module  310  calculates the weighting of the task node by multiplying the first total value by the second total value. 
     It should be noted that the method of calculating the weighting of the task node described above is one example. This embodiment is not limited regarding the method of calculating the weighting of the task nodes that is employed. Any method may be employed, as long as the method is capable of calculating an index (weighting) for evaluating the importance level of the task nodes based on the configuration of the physical layer and the configuration of the logical layer. 
     Next, the weighting calculation module  310  updates the weighting management table  327  based on the calculated weighting (Step S 203 ). 
     Specifically, the weighting calculation module  310  adds an entry to the weighting management table  327 , and sets a predetermined identifier in the identifier  1201  of the added entry. The weighting calculation module  310  sets “node” in the type  1202  of the added entry, and sets the identifier of the task node to be processed in the task node identifier  1204 . Further, the weighting calculation module  310  sets the calculated weighting in the weighting  1205 . It should be noted that the parameter type  1203  remains blank. 
     The weighting calculation module  310  then determines whether or not the weighting of the task node is more than a threshold (Step S 204 ). 
     Specifically, the weighting calculation module  310  refers to the threshold management table  324 , and retrieves an entry having a task identifier  902  that matches the task identifier  805  of the entry selected from the node management table  323 . The weighting calculation module  310  then determines whether or not the weighting of the task node is more than the value of the threshold  903  of the retrieved entry. 
     When the weighting of the task node is determined to be equal to or less than the value of the threshold  903 , the weighting calculation module  310  proceeds the processing to Step S 206 . 
     When the weighting of the task node is determined to be more than the value of the threshold  903 , the weighting calculation module  310  registers the selected task node in a verification list (Step S 205 ). In this case, the verification list is a list in which the important nodes are registered. 
     Specifically, the weighting calculation module  310  registers the entry of the selected task node in the verification list. In this embodiment, the weighting calculation module  310  registers an entry having the same content as the entry corresponding to the task node of the logical configuration management table  321  in the verification list. Further, the weighting calculation module  310  sorts the entries stored in the verification list based on the weighting of the task node. In this embodiment, the weighting calculation module  310  rearranges the entries in descending order of the task node weighting. 
     It should be noted that when there is no verification list, the weighting calculation module  310  generates the verification list in a work area of the memory  302 , and registers the entry for the task node in the generated verification list. 
     The weighting calculation module  310  then determines whether or not the processing of all of the task nodes in the operations system to be processed is complete (Step S 206 ). When it is determined that the processing of all of the task nodes in the operations system to be processed is not complete, the processing returns to Step S 200 , and the weighting calculation module  310  executes the same processing on a new task node. 
     When it is determined that the processing of all of the task nodes in the operations system to be processed is complete, the weighting calculation module  310  finishes the processing. 
       FIG. 17  is a flowchart for illustrating an example of the measurement processing of the first embodiment. The measurement module  311  included in the control module  201  executes the measurement processing. 
     The measurement module  311  starts loop processing of the important nodes (Step S 300 ). At this stage, the measurement module  311  selects one important node entry from the verification list. In this embodiment, the entries in the verification list are arranged in descending order of the weighting, and hence the measurement module  311  selects an entry in order from the top of the verification list. Further, the measurement module  311  adds an entry to the system performance table  326 , sets a predetermined identifier in the identifier  1101 , and sets the identifier of the selected important node in the important node identifier  1102 . 
     The measurement module  311  refers to the adjustment method management table  325 , and specifies the verification method to be applied to the selected important node (Step S 301 ). Specifically, processing such as the following is executed. 
     The measurement module  311  obtains the identifier from the assigned node identifier  803  of the entry of the selected important node. 
     (1) When the obtained identifier is an identifier of a physical node, the measurement module  311  specifies the apparatus based on the physical node identifier. When the apparatus is a blade server  223 , the measurement module  311  obtains all of the entries having “physical server” in the node type  1002  from the adjustment method management table  325 . Further, when the apparatus is a switch, the measurement module  311  obtains all of the entries having “SW” in the node type  1002  from the adjustment method management table  325 .
 
(2) When the obtained identifier is an identifier of a logical node, the measurement module  311  specifies the logical apparatus based on the logical node identifier. When the logical apparatus is a VM, the measurement module  311  obtains all of the entries having “VM” in the node type  1002  from the adjustment method management table  325 . Further, when the logical apparatus is a virtual switch, the measurement module  311  obtains all of the entries having “vSW” in the node type  1002  from the adjustment method management table  325 .
 
     In addition, the measurement module  311  refers to the topology management table  320 , specifies the physical node associated with the logical node, and by using the same method as the method described in (1), obtains all of the entries corresponding to the physical node from the adjustment method management table  325 . 
     The above is a description of the processing carried out in Step S 301 . 
     Next, the measurement module  311  starts loop processing of the verification method (from Step S 302  to Step S 309 ). In the loop processing of the verification method, the measurement module  311  adjusts at least any one of the parameters of the physical node and logical node for each specified verification method, and measures the load on the overall operations system. 
     First, the measurement module  311  selects one of the specified verification methods, and starts measurement processing based on the selected verification method (Step S 302 ). At this stage, when there are a plurality of entries having the same adjustment parameter type  1004  and adjustment value  1005 , the measurement module  311  selects an entry based on the value of the priority  1006 . 
     The measurement module  311  changes a parameter of the physical node or logical node based on the selected verification method (Step S 303 ), and measures various loads in the operations system (Step S 304 ). 
     The measurement method employed in this embodiment is not limited. For example, an experimental design method may be employed. In this embodiment, the load of each task node in the operations system is measured. For example, the measurement module  311  measures the processor usage, memory usage, network bandwidth usage, throughput, and the like of the task node as the load. In this case, as an example, the number of processed requests (throughput) at the task node is measured as the load. 
     Further, the measurement module  311  stores a measurement result  1800  in a work area of the memory  302 , or in a storage area of the storage subsystem  260 . An example of the measurement result  1800  is now described with reference to  FIG. 18 . 
       FIG. 18  is an explanatory diagram for illustrating an example of the measurement result  1800  of the first embodiment. 
     The measurement result  1800  shown in  FIG. 18  has one entry for each entry in the verification list. In other words, the measurement result  1800  includes the same number of entries as important nodes. Each entry includes an important node identifier  1801 , an adjustment parameter type  1802 , a task node identifier  1803 , a parameter value  1804 , and a measurement value  1805 . 
     The important node identifier  1801  is an identifier of the important node selected in Step S 300 . The identifier stored in the important node identifier  1801  is the same as the identifier stored in the task node identifier  801 . The adjustment parameter type  1802  is the type of adjustment parameter adjusted in Step S 303 . The adjustment parameter type  1802  is the same as the adjustment parameter type  1004 . 
     The task node identifier  1803  is an identifier of the task node on which load measurement was carried out. The task node identifier  1803  is the same as the task node identifier  801 . The parameter value  1804  is a value of the parameter actually adjusted based on the verification method selected by the measurement module  311  in Step S 302 . The measurement value  1805  is a value representing the load of the task node corresponding to the task node identifier  1803  when the parameter has been adjusted to the value indicated in the parameter value  1804 . 
     The description now returns to  FIG. 17 . 
     The measurement module  311  determines whether or not processing of all of the specified verification methods is complete (Step S 305 ). When it is determined that processing of all of the specified verification methods is not complete, the processing returns to Step S 302 , and the measurement module  311  executes the same processing on a new verification method. 
     When it is determined that processing of all of the specified verification methods is complete, the measurement module  311  determines whether or not processing of all of the important nodes is complete, that is, whether or not processing of all of the entries included in the verification list is complete (Step S 306 ). When it is determined that processing of all of the important nodes is not complete, the processing returns to Step S 300 , and the measurement module  311  executes the same processing on a new important node. 
     When it is determined that processing of all of the important nodes is complete, the measurement module  311  updates the system performance table  326  based on the measurement result  1800  (Step S 307 ), and then finishes the processing. Specifically, processing such as the following is executed. 
     The measurement module  311  reads one entry from the measurement result  1800 , and adds one entry to the system performance table  326 . The measurement module  311  sets a predetermined identifier in the identifier  1101  of the added entry, and sets in the important node identifier  1102  the identifier stored in the important node identifier  1801 . 
     The measurement module  311  selects one adjustment parameter type  1802 , and specifies a task node having a load that has changed by a fixed value or more. For example, the measurement module  311  calculates the rate of change in the load by analyzing the measurement value  1805  corresponding to the task node identifier  1803 , and determines whether or not the calculated rate of change in the load is 20% or more. A task node having a calculated rate of change in the load of 20% or more is specified as a task node having a load that has changed by a fixed value or more. 
     It should be noted that when a task node having an association with an important node is known in advance based on the result of a previous stress test and the like, that is, when the system performance table  326  used in a previous stress test is present in the memory  302 , the measurement module  311  may select that task node. 
     The measurement module  311  sets the identifier of the task node specified by the above-mentioned processing in the task node identifier  1803  of the entry added to the system performance table  326 . Further, the measurement module  311  sets information stored in the adjustment parameter type  1802  selected in the above-mentioned processing in the adjustment parameter type  1105  of that entry. 
     The measurement module  311  calculates the performance characteristic function based on an analysis result of the above-mentioned processing. In this case, the measurement module  311  calculates the performance characteristic function based on the value of the adjustment parameter corresponding to the adjustment parameter type  1105  as the domain (X) and the throughput as the range (Y). The measurement module  311  sets the calculated function in the function  1106 . Then, the system performance table  326  is updated based on the same procedure. 
     The above is a description of the processing carried out in Step S 307 . 
       FIG. 19  is a flowchart for illustrating an example of the counting processing of the first embodiment. The estimation module  312  included in the control module  201  executes the counting processing. 
     First, the estimation module  312  starts loop processing of the important nodes (Step S 400 ). At this stage, the estimation module  312  selects one entry from the system performance table  326 . In the following description, the selected entry is also referred to as an important node entry. 
     Next, the estimation module  312  starts loop processing of the adjustment parameters (Step S 401 ). Specifically, the estimation module  312  extracts all of the types of adjustment parameters from the adjustment parameter type  1105  of the important node entry, and selects one adjustment parameter to be processed from among the extracted adjustment parameters. 
     Next, the estimation module  312  starts loop processing of the task nodes (Step S 402 ). Specifically, processing such as the following is executed. 
     The estimation module  312  extracts the associated node identifier from the task node identifier  1803  of the row including the selected adjustment parameter in the adjustment parameter type  1105  of the important node entry. The estimation module  312  selects one associated node identifier from among the extracted task node identifiers. 
     The estimation module  312  refers to the node management table  323  and specifies another task node that is connected to the selected task node. The estimation module  312  calculates the number of task nodes passed through from the important node until the selected task node as a first hop count. Further, the estimation module  312  calculates the number of task nodes passed through from the important node until the specified task node as a second hop count. 
     The estimation module  312  determines whether or not the first hop count is larger than the second hop count. When the first hop count is equal to or less than the second hop count, the estimation module  312  proceeds the processing to Step S 403 . When the first hop count is larger than the second hop count, the estimation module  312  proceeds the processing to Step S 405 . The above is a description of the processing carried out in Step S 402 . 
     The estimation module  312  calculates the weighting of the edge connecting two task nodes (Step S 403 ). Various methods may be employed as the method of calculating the weighting of the edge. For example, a method such as the following may be used. 
     The estimation module  312  obtains the performance characteristic function from the function  1106  of the row matching the important node, associated node, and adjustment parameter selected in Step S 400  to Step S 402 . The estimation module  312  calculates, as the weighting of the edge, a maximum value of the derivative of the performance characteristic function based on the performance characteristic function and the domain of the variable X. 
     It should be noted that, depending on the type of performance characteristic function, there may be cases in which the derivative cannot be calculated. In that case, a method can be employed that uses, instead of the derivative, the average value, the total value, or the like of the size of the load of the associated nodes resulting from a change in the adjustment parameter. In this case, the estimation module  312  refers to the measurement result  1800 , and obtains the required values from the measurement value  1805  matching the identifiers of the important node, the task node, and the adjustment parameter. The above is a description of the processing carried out in Step S 403 . 
     The estimation module  312  updates the weighting management table  327  based on the calculated edge weighting (Step S 404 ). 
     Specifically, the estimation module  312  adds an entry to the weighting management table  327 , and sets a predetermined identifier in the identifier  1201  of the added entry. The estimation module  312  sets “edge” in the type  1202  of the added entry, and sets the selected adjustment parameter in the parameter type  1203 . Further, the estimation module  312  sets the identifiers of the selected task node and the specified task node in the task node identifier  1204 , and sets the calculated edge weighting in the weighting  1205 . It should be noted that when a plurality of task nodes are connected to the selected task node, a plurality of entries are added to the weighting management table  327 . 
     The estimation module  312  determines whether or not processing of all of the associated nodes is complete (Step S 405 ). When it is determined that processing of all of the associated nodes is not complete, the processing returns to Step S 402 , and the estimation module  312  executes the same processing on a new associated node. 
     When it is determined that processing of all of the associated nodes is complete, the estimation module  312  determines whether or not processing of all of the adjustment parameters is complete (Step S 406 ). When it is determined that processing of all of the adjustment parameters is not complete, the processing returns to Step S 401 , and the estimation module  312  selects a new adjustment parameter and executes the same processing. 
     When it is determined that processing of all of the adjustment parameters is complete, the estimation module  312  determines whether or not processing of all of the important nodes is complete (Step S 407 ). When it is determined that processing of all of the important nodes is not complete, the processing returns to Step S 400 , and the estimation module  312  executes the same processing on a new important node. On the other hand, when it is determined that processing of all of the important nodes is complete, the estimation module  312  proceeds the processing to Step S 408 . 
     In the processing from Step S 408  onwards, the estimation module  312  generates a matrix representing the strength of the associations among the plurality of task nodes based on the weighting management table  327 . In the following description, the matrix representing the strength of the associations among the plurality of task nodes is also referred to as an association matrix. In this embodiment, an edge weighting is calculated for each adjustment parameter, and hence the estimation module  312  generates an association matrix for each adjustment parameter. 
     The estimation module  312  selects the adjustment parameter to be processed (Step S 408 ). Specifically, the estimation module  312  extracts all the types of adjustment parameters stored in the parameter type  1203  of the weighting management table  327 , and selects the adjustment parameter to be processed from among the extracted adjustment parameters. 
     The estimation module  312  generates the association matrix for the selected adjustment parameter (Step S 409 ). Specifically, processing such as the following is executed. 
     The estimation module  312  generates a matrix having n-rows and n-columns. In this case, “n” represents the number of task nodes, which matches the number of task nodes registered in the weighting management table  327 . At this stage, all of the values of the matrix elements are set to “0”. 
     In this embodiment, the identifier of the task node corresponds to the row and column of the matrix. For example, “task node 1” corresponds to row 1, column 1. In this case, the matrix component at row 1, column 1, represents the strength of the association of the “task node 1” itself. The matrix component at row 1, column n, or row n, column 1, represents the strength of the association between the “task node 1” and another task node. 
     The estimation module  312  refers to the weighting management table  327 , and sets the value stored in the weighting  1205  of the entry having “node” in the type  1202  as the diagonal components of the matrix. In this embodiment, the diagonal components of the matrix components do not depend on the adjustment parameter. 
     The estimation module  312  refers to the weighting management table  327 , and retrieves an entry having “edge” stored in the type  1202  and the selected adjustment parameter stored in the parameter type  1203 . Further, the estimation module  312  sets the value stored in the weighting  1205  as the off-diagonal components of the matrix based on the task node identifier  1204  of the retrieved entry. 
     According to the above-mentioned processing, an association matrix such as that shown in  FIG. 13  can be generated from the weighting management table  327 . 
     The above is a description of the processing carried out in Step S 409 . 
     Next, the estimation module  312  specifies the impact range in the operations system based on the generated association matrix (Step S 410 ). 
     Specifically, the estimation module  312  specifies a matrix component in which a value larger than a predetermined threshold is set. The estimation module  312  can specify the impact range of the operations system caused by fluctuations in the load of the important nodes based on an estimated matrix component. The estimation module  312  stores impact range data in which the identifier of the important node, the adjustment parameter, and the specified matrix component are associated with one another in a work area of the memory  302 . 
     When the management server  100  receives an instruction from the user to display the impact range, the management server  100  displays an operations system such as that illustrated in  FIG. 1  based on the node management table  323  and the association matrix. Further, based on the impact range data, the management server  100  can display an impact range such as that illustrated in  FIG. 1 . 
     It should be noted that the instruction from the user includes at least any one of the identifier of the important node and the adjustment parameter. 
     The above is a description of the processing carried out in Step S 410 . 
     Next, the estimation module  312  determines whether or not processing of all of the adjustment parameters is complete (Step S 411 ). When it is determined that processing of all of the adjustment parameters is not complete, the processing returns to Step S 408 , and the estimation module  312  executes the same processing on a new adjustment parameter. 
     When it is determined that processing of all of the adjustment parameters is complete, the estimation module  312  finishes the processing. 
       FIG. 20  is a flowchart for illustrating an example of the monitoring processing of the first embodiment. The operations system monitoring module  313  included in the control module  201  executes the monitoring processing. 
     The monitoring processing starts when the operations system monitoring module  313  detects a processing start trigger (Step S 500 ). For example, when the monitoring processing is executed periodically, the operations system monitoring module  313  starts the monitoring processing when detecting a lapse of a fixed period of time. Further, the operations system monitoring module  313  starts the monitoring processing when an instruction is received from the user. 
     The operations system monitoring module  313  reads the system performance table  326  and the impact range data, and monitors the state of the task nodes included in the impact range in the operations system based on the read information (Step S 501 ). 
     It should be noted that various methods may be employed as the method of monitoring the load. For example, the operations system monitoring module  313  may obtain the state of the task nodes each time a predetermined time interval elapses. Further, in this embodiment, the state of the task nodes included in the impact range is monitored. However, only the state of the important nodes may be monitored. In addition, the monitoring may be carried out on only a specific parameter, or on all parameters. 
     The operations system monitoring module  313  displays the loads of the task nodes included in the impact range as a meter representing a load ratio (Step S 502 ). In other words, a critical level representing the possibility of the computer resources allocated to the task nodes being insufficient is displayed as a meter. Specifically, processing such as the following is executed. 
     The operations system monitoring module  313  refers to the system performance table  326 , and retrieves an entry matching the identifier of an important node included in the impact range data. Next, the operations system monitoring module  313  obtains the performance characteristic function from the function  1106  of a row having an identifier stored in the associated node identifier  1103  that matches the identifier of the task node for which a load has been obtained, and an adjustment parameter type  1105  that matches the parameter corresponding to the obtained load. 
     The operations system monitoring module  313  calculates the current load ratio with respect to the maximum load based on the performance characteristic function and the value of the obtained load of the task node. It should be noted that the maximum load can be calculated based on the domain X and the performance characteristic function. 
     The operations system monitoring module  313  displays a meter  15  such as that illustrated in  FIG. 1  based on the calculated load ratio. The above is a description of the processing carried out in Step S 502 . 
     Next, the operations system monitoring module  313  determines whether or not there is a task node having an increased load (Step S 503 ). 
     Specifically, the operations system monitoring module  313  determines whether or not there is a task node having a load ratio calculated in Step S 502  that is larger than a predetermined threshold. For example, the operations system monitoring module  313  determines whether or not there is a task node having a load ratio larger than 80%. 
     Further, the operations system monitoring module  313  may determine that there is a task node having an increased load when an instruction to change the configuration of the task nodes has been received from the user who has seen the display of the meter  15 . 
     When it is determined that there are no task nodes having an increased load, the processing returns to Step S 501 , and the operations system monitoring module  313  continues to monitor the state of the operations system. 
     When it is determined that there is a task node having an increased load, the operations system monitoring module  313  refers to the rule management table  328 , and retrieves a rule that is applicable to the task node (Step S 504 ). Specifically, processing such as the following is executed. 
     The operations system monitoring module  313  refers to the node management table  323 , and retrieves an entry having an identifier of the task node identifier  801  that matches the identifier of the task node specified in Step S 504 . The operations system monitoring module  313  obtains the values stored in the task type  804  and the task identifier  805  of the retrieved entry. 
     The operations system monitoring module  313  refers to the rule management table  328 , and retrieves an entry having a task identifier  1401  that matches the value obtained from the task identifier  805  and a task type  1403  that matches the value obtained from the task type  804 . 
     The above is a description of the processing carried out in Step S 504 . It should be noted that when there are a plurality of task nodes having an increased load, the operations system monitoring module  313  executes the above-mentioned processing on each of those task nodes. 
     Next, the operations system monitoring module  313  displays the rule retrieved from the rule management table  328  along with an alert (Step S 505 ). Then, the processing returns to Step S 501 , and the operations system monitoring module  313  continues to monitor the operations system. 
     Specifically, the operations system monitoring module  313  displays to the user the content stored in the rule  1406  and the numeral value set in the priority order  1405  of the entry retrieved from the rule management table  328 . As a result, the user can grasp the fact that improvement to the performance of the operations system is necessary, and grasp the change content for improving the performance of the operations system, without needing to grasp the detailed state of the configuration of the physical layer and logical layer. 
     It should be noted that in this embodiment, the operations system monitoring module  313  only displays the rule. However, processing for changing the configuration of the operations system may be executed based on the rule. Further, the rule may be displayed along with cost information, such as the cost when the rule is applied and a service suspension time. 
     It should be noted that this embodiment is described based on an example of a system having a three-layer configuration, namely, a physical layer, a logical layer, and a task layer. However, this embodiment is not limited to such a configuration. This embodiment may also be applied in a system having a two-layer configuration consisting of a physical layer and a task layer. In other words, this embodiment may be applied even in a system in which a physical node is assigned to every task node. 
     It should also be noted that the logical layer may have a multi-level structure. For example, the logical layer may have a two-level structure in which a plurality of logical computers are built by logically partitioning a blade server  223 , and a plurality of virtual computers are made to run on each of the logical computers. 
     According to the first embodiment, the management server  100  can estimate the performance limit of the operations system by specifying important task nodes based on configuration information on the physical layer and logical layer, and executing a stress test on the important task nodes. An example of a method of applying the first embodiment is described below. 
     The performance limit of a computer system for a specific operations system can be estimated by building the specific operations system on the computer system, and executing node selection processing, measurement processing, and counting processing by the management server  100 . 
     As a result, the provider operating the computer system can estimate the computer resources required to build the specific operations system, and can design a computer system specifically adapted to the specific operations system. 
     Further, the execution of the monitoring processing by the management server  100  enables the management costs of the user operating the operations system to be reduced. 
     Modified Example 
     In the node selection processing, the weighting calculation module  310  only calculates the weighting of the selected task node. However, the weighting calculation module  310  may also calculate the weighting of an edge connecting the selected task node and another task node. 
     An example is described below of a case in which the logical node assigned to a selected task node (first task node) forms a cluster with another logical node of the same physical server, and a task node (second task node) assigned to the another logical node exists. 
     In Step S 201 , the weighting calculation module  310  refers to the topology management table  320  and the logical configuration management table  321 , and determines whether or not there is a special connection relationship between the selected task node and the another task node based on the identifiers stored in the task node identifier  801  and the connected node identifier  806  of the entry corresponding to the selected task node. 
     When it is determined that there is a special connection relationship, in Step S 202 , the weighting calculation module  310  calculates the eigen value stored in the system configuration  608  as the weighting of the edge connecting the first task node and the second task node. 
     In Step S 203 , the weighting calculation module  310  registers the calculated edge weighting in the weighting management table  327 . Specifically, processing such as the following is executed. 
     The weighting calculation module  310  adds an entry to the weighting management table  327 , and sets a predetermined identifier in the identifier  1201  of the added entry. The weighting calculation module  310  sets “edge” in the type  1202  of the added entry, and sets the identifier of the first task node and the identifier of the second task node in the task node identifier  1204 . It should be noted that the parameter type  1203  remains blank. 
     Further, the weighting calculation module  310  sets a conversion formula that uses the calculated edge weighting as a weighting coefficient in the weighting  1205  of the added entry. A conversion formula such as the weighting  1205  of the entry having “102” in the identifier  1201  of  FIG. 12  is set. In this case, “0.8” is the weighting coefficient, and “m” is the weighting of the edge determined based on the measurement result. At this point, the edge weighting is unknown, and hence the edge weighting is set as a variable. It should be noted that the conversion formula is not limited to a conversion formula such as that shown in  FIG. 12 . For example, a conversion formula involving addition, division, and the like may also be employed. 
     The remaining processing is the same as the processing in the first embodiment, and hence a description thereof is omitted here. It should be noted that the same processing may be applied on the physical nodes. 
     In the counting processing, in Step S 404 , the estimation module  312  calculates a corrected edge weighting by substituting the calculated edge weighting into the conversion formula stored in the weighting  1205 , and overwrites the weighting in the weighting  1205  with the calculated edge weighting. 
     Thus, with the use of a weighting coefficient, a measurement result that better reflects the configuration of the physical layer and logical layer can be obtained. 
     Second Embodiment 
     In the measurement processing of the first embodiment, the management server  100  measures the load of the task nodes for all of the verification methods. However, there are cases in which the adjustment parameter is obvious in advance, or in which faster measurement processing is necessary. In a second embodiment of this invention, the user designates the adjustment parameter in advance. The second embodiment is described while focusing on the differences from the first embodiment. 
     The system configuration, the configuration of the management server  100 , and the configuration of the blade servers  223  in the second embodiment are the same as in the first embodiment, and hence a description thereof is omitted here. Further, the content of the management table group  202  in the management server  100  in the second embodiment is also the same as in the first embodiment, and hence a description thereof is omitted here. In addition, the node selection processing, the counting processing, and the monitoring processing in the second embodiment are the same as in the first embodiment, and hence a description thereof is omitted here. 
     In the second embodiment, a part of the processing content of the measurement processing is different. Specifically, the measurement module  311  displays to the user an orientation parameter registration screen  2100  for designating the verification method to be applied when starting the measurement processing. The orientation parameter registration screen  2100  is now described. 
       FIG. 21  is an explanatory diagram for illustrating an example of the orientation parameter registration screen  2100  of the second embodiment. 
     A selection item display area  2110 , a register button  2120 , a registration content display area  2130 , a setting button  2140 , and a cancel button  2150  are displayed on the orientation parameter registration screen  2100 . 
     The selection item display area  2110  is an area for displaying information for designating the adjustment parameter. The selection item display area  2110  includes a task node selection item  2111  and a parameter orientation selection item  2112 . 
     The task node selection item  2111  is an item for selecting an important node. The parameter orientation direction selection item  2112  is an item for designating the adjustment parameter to be applied on the important node. The measurement module  311  displays the important node in the task node selection item  2111  based on the verification list. The parameter orientation selection item  2112  displays an adjustment parameter condition, the type of the adjustment parameter, or the like, rather than displaying the adjustment parameter itself. For example, “processor-oriented” or the like is displayed as a display for designating the adjustment parameter associated with the processor. 
     In the example illustrated in  FIG. 21 , the task node selection item  2111  and the parameter orientation selection item  2112  are displayed as pull-down menus. 
     The register button  2120  is an operation button for registering the operation content of the selection item display area  2110 . When the user presses the register button  2120 , the content set in the selection item display area  2110  is displayed in the registration content display area  2130 . 
     The registration content display area  2130  is an area for displaying the setting content of the selection item display area  2110 . The registration content display area  2130  includes a task node identifier  2131  and a parameter orientation  2132 . 
     The task node identifier  2131  is the identifier of the important node. The parameter orientation  2132  is the parameter orientation to be applied on the important node. 
     The setting button  2140  is an operation button for reflecting the content displayed in the registration content display area  2130  in the measurement processing. When the user presses the setting button  2140 , the measurement module  311  starts the processing of Step S 300 . The cancel button  2150  is an operation button for cancelling the content displayed in the registration content display area  2130 . 
     When the setting button  2140  is pressed, the measurement module  311  temporarily stores the content displayed in the registration content display area  2130  in a work area of the memory  302 . Further, the measurement module  311  specifies the method of verifying the selected important node based on the content set by using the orientation parameter registration screen  2100  (Step S 301 ). 
     For example, when “processor-oriented” is designated for the selected important node, the measurement module  311  retrieves only the verification methods having an adjustment parameter type  1004  that is associated with the processor from among the verification methods registered in the adjustment method management table  325 . The remaining processing is the same as in the first embodiment, and hence a description thereof is omitted here. 
     According to the second embodiment, the management server  100  is capable of executing a stress test for only the necessary adjustment parameters. As a result, the load of the measurement processing can be reduced, and the speed of the measurement processing can be increased. 
     Third Embodiment 
     In a third embodiment of this invention, in the node selection processing, the management server  100  selects the important nodes by using the configuration of the task layer in addition to using the configuration of the physical layer and logical layer. The third embodiment is described while focusing on the differences from the first embodiment. 
     The system configuration, the configuration of the management server  100 , and the configuration of the blade servers  223  in the third embodiment are the same as in the first embodiment, and hence a description thereof is omitted here. The content of the management table group  202  in the management server  100  in the third embodiment is also the same as in the first embodiment, and hence a description thereof is omitted here. In addition, the measurement processing, counting processing, and monitoring processing are the same as in the first embodiment, and hence a description thereof is omitted here. In the third embodiment, a part of the node selection processing is different from the first embodiment. 
     After selection of the task node (Step S 200 ), the weighting calculation module  310  obtains the weighting associated with the selected task node from the topology management table  320 , the logical configuration management table  321 , and the task management table  322  (Step S 201 ). 
     The method of obtaining the eigen values from the topology management table  320  and the logical configuration management table  321  is the same as in the first embodiment, and hence a description thereof is omitted here. In this case, a method in which the weighting calculation module  310  obtains the eigen value of the task node from the task management table  322  is described. The weighting calculation module  310  refers to the task management table  322 , and retrieves an entry having a task identifier  701  that matches the task identifier  805  of the entry selected from the node management table  323 . The weighting calculation module  310  obtains the value of the eigen value  706  of the retrieved entry. 
     In Step S 202 , the weighting calculation module  310  calculates the weighting of the task node by using the eigen values obtained from the topology management table  320 , the logical configuration management table  321 , and the task management table  322 . The remaining processing is the same as in the first embodiment, and hence a description thereof is omitted here. 
     According to the third embodiment, the selection of the important nodes can be performed while also considering information on the task layer, and hence a more accurate performance limit of the operations system can be estimated. An example of a method of providing the third embodiment is described below. 
     The performance limit of the operations system with respect to a known computer system can be estimated by building a new operations system on a known computer system, and executing node selection processing, measurement processing, and counting processing by the management server  100 . 
     As a result, the user operating the operations system can grasp the computer resources required for the operations system, the configuration to be adjusted, the setting items to be adjusted, and the like. 
     Further, the execution of the monitoring processing by the management server  100  enables the management costs of the user operating the operations system to be reduced. 
     Fourth Embodiment 
     In a fourth embodiment of this invention, the management server  100  reflects the configuration of the task layer in an association matrix in the counting processing. 
     The system configuration, the configuration of the management server  100 , and the configuration of the blade servers  223  in the fourth embodiment are the same as in the first embodiment, and hence a description thereof is omitted here. Further, the content of the management table group  202  in the management server  100  in the fourth embodiment is also the same as in the first embodiment, and hence a description thereof is omitted here. The node selection processing, counting processing, and monitoring processing in the fourth embodiment are the same as in the first embodiment, and hence a description thereof is omitted here. In the fourth embodiment, a part of the counting processing is different from the first embodiment. 
     The processing from Step S 400  to Step S 407  is the same as in the first embodiment. 
     After the estimation module  312  has selected the adjustment parameter to be processed (Step S 408 ), the estimation module  312  generates an association matrix (Step S 409 ). Specifically, processing such as the following is executed. 
     First, the estimation module  312  generates an association matrix based on the weighting management table  327  in accordance with the same procedure as in the first embodiment. Next, the estimation module  312  generates a superposition matrix based on configuration information on the task layer. The estimation module  312  reflects the superposition matrix in the association matrix. For example, the estimation module  312  multiplies, or, adds the superposition matrix to the association matrix. 
     In Step S 410 , the estimation module  312  specifies an impact range based on the association matrix in which the superposition matrix is reflected. 
     In this case, an example of the processing for generating the superposition matrix is described with reference to  FIG. 22 .  FIG. 22  is a flowchart for illustrating an example of the processing for generating the superposition matrix of the fourth embodiment. 
     The estimation module  312  generates a matrix having n-rows and n-columns in which all of the matrix components are “0” (Step S 600 ). The estimation module  312  sets the eigen value of the task node for the diagonal components (Step S 601 ). 
     Specifically, the estimation module  312  selects one entry from the node management table  323 , and obtains the value of the task identifier  805  of the selected entry. The estimation module  312  refers to the task management table  322 , and retrieves an entry having a task identifier  701  that matches the value obtained from the task identifier  805 . The estimation module  312  obtains the value of the eigen value  706  of the retrieved entry, and sets the value of the eigen value  706  for the diagonal components of the matrix corresponding to the selected task node. 
     The estimation module  312  executes the above-mentioned processing on all of the entries in the node management table  323 , that is, on all of the task nodes. As a result, the values for the diagonal components of the matrix are set. 
     Next, the estimation module  312  starts loop processing of the task nodes (Step S 602 ). At this stage, the estimation module  312  selects one entry from the node management table  323 . 
     The estimation module  312  specifies the associated nodes connected to the task node corresponding to the selected entry (Step S 603 ). Specifically, the estimation module  312  obtains the identifier of the task node from the connected node identifier  806  of the selected entry. In the following description, the task node corresponding to the connected node identifier  806  is also referred to as a connected node. 
     The estimation module  312  starts loop processing of the specified connected nodes (Step S 604 ). At this stage, the estimation module  312  selects one identifier of the connected node to be processed from among the obtained identifiers of the connected nodes. 
     The estimation module  312  confirms the configuration associated with the task node and connected node (Step S 605 ), and determines whether or not there is a special connection relationship between the task node and the connected node (Step S 606 ). Specifically, processing such as the following is executed. 
     First, the estimation module  312  confirms the configuration of the task node. The estimation module  312  refers to the entry corresponding to the task node in the node management table  323 , obtains the value of the assigned node identifier  803  and the value of the task identifier  805 , and obtains information on the associated information  807 . 
     The estimation module  312  refers to the task management table  322 , and retrieves the entry having a value of the task identifier  701  that matches the value obtained from the task identifier  805 . The estimation module  312  obtains the eigen value  706  of the retrieved entry. 
     The estimation module  312  specifies the node assigned to the task node based on the value obtained from the assigned node identifier  803 . 
     When a physical node is assigned for the task node, the estimation module  312  refers to the topology management table  320 , and retrieves the entry having a value of the physical node identifier  503  that matches the value of the assigned node identifier. The estimation module  312  obtains information from the reliability type  507  of the retrieved entry, and obtains the value from the eigen value  508 . 
     When a logical node is assigned for the task node, the estimation module  312  refers to the logical configuration management table  321 , and retrieves the entry having a value of the logical node identifier  603  that matches the value of the assigned node identifier. The estimation module  312  obtains information from the system configuration  608  of the retrieved entry, and obtains the value from the eigen value  610 . Further, based on the value of the physical node identifier  607  of the retrieved entry, the estimation module  312  refers to the topology management table  320 , obtains information from the reliability type  507  of the entry corresponding to the physical node, and obtains the value from the eigen value  508 . 
     The same processing is also carried out on the configuration of the connected node. 
     The estimation module  312  analyzes the configuration of the task node and connected node, and determines whether or not there is a special connection relationship. For example, when the task node and the connected node are each logical nodes created on the same physical server and forming a cluster, the estimation module  312  determines that there is a special connection relationship. 
     The above is a description of the processing carried out in Step S 605  and Step S 606 . 
     When it is determined that there is not a special connection relationship between the task node and the connected node, the estimation module  312  proceeds the processing to Step S 609 . 
     When it is determined that there is a special connection relationship between the task node and the connected node, the estimation module  312  calculates the eigen value of the edge by using the eigen value obtained in Step S 605  (Step S 607 ). Various methods of calculating the eigen value of the edge may be employed. For example, the eigen value of the edge may be calculated by adding together the value of the eigen value  610  associated with the task node and the value of the eigen value  610  associated with the connected node. It should be noted that in this embodiment, the method of calculating the eigen value of the edge is not limited. 
     The estimation module  312  sets the calculated eigen value of the edge for the diagonal components corresponding to the edge (Step S 608 ). 
     The estimation module  312  determines whether or not the processing of all of the connected nodes specified in Step S 603  is complete (Step S 609 ). When it is determined that the processing of all of the connected nodes specified in Step S 603  is not complete, the processing returns to Step S 604 , and the estimation module  312  executes the same processing on a new connected node. 
     When it is determined that the processing of all of the connected nodes specified in Step S 603  is complete, the estimation module  312  determines whether or not the processing of all of the task nodes is complete (Step S 610 ). 
     When it is determined that the processing of all of the task nodes is not complete, the processing returns to Step S 602 , and the estimation module  312  executes the same processing on a new task node. On the other hand, when it is determined that the processing of all of the task nodes is complete, the estimation module  312  finishes the processing. 
     The above is a description of the generation processing of the superposition matrix. 
     According to the fourth embodiment, the performance limit of the operations system can be estimated in consideration of information on the physical layer, logical layer, and task layer. It should be noted that the fourth embodiment may be provided using the same method as in the third embodiment. 
     Fifth Embodiment 
     In a fifth embodiment of this invention, the management server  100  incorporates impact range data into template information to be used when building the operations system. 
     In the related art, the configuration of the operations system is managed as template information. The template information is, for example, data in an extensible markup language (XML) format. The management server  100  incorporates the result of counting processing into template information corresponding to an operations system that has carried out a stress test. 
     The system configuration and the configuration of the blade servers  223  in the fifth embodiment are the same as in the first embodiment, and hence a description thereof is omitted here. A part of the configuration of the management server  100  of the fifth embodiment is different. 
       FIG. 23  is a block diagram for illustrating a configuration example of the management server  100  of the fifth embodiment. 
     The hardware configuration of the management server  100  is the same as in the first embodiment, and hence a description thereof is omitted here. In the fifth embodiment, the control module  201  includes a meta tag insertion module  2301 . Further, the management table group  202  of the fifth embodiment includes template management information  2302 . 
     The template management information  2302  stores information on the configuration of the operations system. Specifically, the template management information  2302  includes a plurality of entries in which identification information on the operations system and template information representing the configuration of the operations system are associated with each other. In this embodiment, the template information is data in the XML format. 
     The meta tag insertion module  2301  is configured to generate a meta tag (meta information) based on the result of the counting processing, and insert the generated meta tag into a meta tag of the template information. Details of the processing executed by the meta tag insertion module  2301  are described later with reference to  FIG. 24 . 
     The remaining configuration is the same as in the first embodiment, and hence a description thereof is omitted here. Further, the node selection processing, measurement processing, counting processing, and monitoring processing of the fifth embodiment are the same as in the first embodiment, and hence a description thereof is omitted here. 
       FIG. 24  is a flowchart for illustrating an example of the meta tag generation processing of the fifth embodiment. 
     The meta tag insertion module  2301  of the control module  201  starts the meta tag generation processing after the counting processing has finished. 
     First, the meta tag insertion module  2301  starts loop processing of the edges (Step S 700 ). At this stage, the meta tag insertion module  2301  refers to the weighting management table  327 , and selects one entry having “edge” stored in the type  1202 . 
     The meta tag insertion module  2301  specifies two task nodes that are connected via the edge corresponding to the selected entry (Step S 701 ). 
     Specifically, the meta tag insertion module  2301  refers to the task node identifier  1204  of the selected entry, and specifies the two task nodes connected via the edge corresponding to the selected entry. 
     The meta tag insertion module  2301  obtains the template information corresponding to the operations system that carried out the stress test from the template management information  2302  (Step S 702 ). The meta tag insertion module  2301  generates a meta tag relating to the edge and a meta tag relating to the two task nodes connected via the edge (Step S 703 ). For example, meta tags such as the following are generated. 
     The meta tag insertion module  2301  generates a meta tag including information on the edge and a meta tag including information on the two task nodes. 
     Further, the meta tag insertion module  2301  refers to the system performance table  326 , and generates a meta tag representing the value of the performance limit of the two task nodes. The meta tag insertion module  2301  retrieves the row having an important node identifier  1102  and an associated node identifier  1103  that match the identifiers of the two task nodes, and obtains the function stored in the function  1106  of that row. In addition, based on the function, the meta tag insertion module  2301  calculates a value X that saturates the response time of the system, and generates a meta tag including the percentile of that value. 
     The meta tag insertion module  2301  inserts the generated meta tag into the obtained template information (Step S 704 ). The meta tag insertion module  2301  determines whether or not processing on all of the edges is complete (Step S 705 ). 
     When it is determined that processing on all of the edges is not complete, the processing returns to Step S 700 , and the meta tag insertion module  2301  executes the same processing on a new edge. When it is determined that processing on all of the edges is complete, the meta tag insertion module  2301  finishes the processing. 
     According to the fifth embodiment, the measurement result of the stress test of the operations system can be inserted into template information to be used when generating the operations system. As a result, a performance characteristic of the operations system can be grasped at the time of building the operations system, and, the system configuration and the like required to build the operations system can be grasped. 
     For example, when creating a virtual computer during building of the operations system, a system that gives consideration to a performance characteristic such as open virtualization format (OVF) can be built. Therefore, the portability and migratability of the system can be improved. 
     Meta tags include a method of responding to any of the physical nodes, logical nodes, and task nodes, and hence when a performance failure has occurred in the computer system or operations system, the failure can be handled in a rapid and correct manner based on the meta tag. 
     This invention is not limited to the above-described embodiments but includes various modifications. The above-described embodiments are explained in details for better understanding of this invention and are not limited to those including all the configurations described above. A part of the configuration of one embodiment may be replaced with that of another embodiment; the configuration of one embodiment may be incorporated to the configuration of another embodiment. A part of the configuration of each embodiment may be added, deleted, or replaced by that of a different configuration. 
     The above-described configurations, functions, processing modules, and processing means, for all or a part of them, may be implemented by hardware: for example, by designing an integrated circuit. The above-described configurations and functions may be implemented by software, which means that a processor interprets and executes programs providing the functions. 
     The information of programs, tables, and files to implement the functions may be stored in a storage device such as a memory, a hard disk drive, or an SSD (a Solid State Drive), or a storage medium such as an IC card, or an SD card. 
     The drawings shows control lines and information lines as considered necessary for explanation but do not show all control lines or information lines in the products. It can be considered that almost of all components are actually interconnected.