Patent Publication Number: US-2011060827-A1

Title: Managing application system load

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application for patent is a Continuation of U.S. patent application Ser. No. 11/773825 filed Jul. 5, 2007, which claims the priority benefit of U.S. Provisional Pat. App. 60/806699 filed Jul. 6, 2006, entitled “Method And Apparatus For Managing Application Storage Load Based On Storage Network Resources”, each of which is incorporated by reference herein as if set forth in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to the field of software application performance and self-managing systems. In particular, it relates to balancing application demands based on the capabilities of the underlying digital computing system including one or more central processing units (CPUs), memory, network and storage area network (SAN). 
     BACKGROUND OF THE INVENTION 
     Applications are commonly hosted on servers that share a common network and storage system through a storage area network (SAN). Imbalance between the demands of the applications and the capabilities of the CPUs, network and SAN has resulted in poor overall performance of the applications sharing the centralized resources. However, individual applications can experience a performance impact if they place too much load on any single element in the subsystem, and particularly the SAN. Further, CPUS, networks and storage arrays are often employed as a shared resource. Multiple applications running on independent servers can impact each other&#39;s performance when subsystem elements are shared among applications. 
     Many applications have internal parameters, which can be set by a user or by a system administrator, which can have a dramatic impact on an application&#39;s performance and throughput. The user typically does not consider the bandwidth sustainable or the parallelism present in the computing system configuration when an application is being initialized to run. A set of default values is commonly used to set the system load. These default values may include, for example, the number of threads, individual application priorities, storage space, and log buffer configuration. These values can also be adjusted during run time. While the values are adjustable by the user, application programmer, or system administrator, there is no guidance provided to adjust the application load in order to better match the characteristics of the underlying computing system resources. 
     Performance of any application can be degraded if an application generates too much traffic for a single device, or if multiple applications flood the system with many requests such that the system is not able to service the aggregate load. The interference generated by one application on another when any element in the system is overloaded can result in large variations in performance. Attempts to provide more predictable application performance often result in the over-provisioning capacity in a particular element in the subsystem. 
     In attempts to solve, or at least minimize, these problems, system administrators can request that each application has a fixed priority. The priority setting is used to “throttle” the application&#39;s demands on the system resources. Unfortunately, assigning a fixed priority can waste resources, and can also lead to application starvation. An alternative to throttling is to manage the quality of service (“QoS”) that each application experiences. The allocation of storage resources may be based upon various criteria, for example, the bandwidth of storage accesses. United States Published Patent Application No. 2005/0089054, which is incorporated herein by reference in its entirety, describes an apparatus for providing QoS based on an allocation of resources. 
     Conventional solutions to the concerns noted above have typically presented their own performance constraints and concerns. Therefore, it would be desirable to provide improved methods, devices, software and systems to more efficiently and flexibly manage the system load generated by an application or applications. 
     SUMMARY OF THE INVENTION 
     One aspect of the invention relates to an improvement in a networked digital computing system, the computing system comprising at least one central processing unit (CPU), a network operable to enable the CPU to communication with other elements of the digital computing system, and a storage area network (SAN) comprising at least one storage device and operable to communicate with the at least one CPU, and wherein the computing system is operable to run at least one application program, the at least one application program having application parameters adjustable to control execution of the application program. In this aspect of the invention, the improvement comprises an Information Resource Manager (IRM) operable to communicate with elements of the digital computing system to obtain performance information regarding operation of and resources available in the computing system, and to utilize this information to enable the IRM to adjust the application parameters relating to application execution, thereby to optimize execution of the at least one application program. 
     The IRM comprises (1) a performance profiling system operable to communicate with the at least one CPU, network and SAN and to obtain therefrom performance information and configuration information, (2) an analytical performance model system, operable to communicate with the performance profiling system and to receive the performance information and configuration information and to utilize the performance information and configuration information to generate an analytical model output, the analytical model output comprising any of performance statistics and updated application parameters, and (3) an application parameter determination system, operable to communicate with the analytical model system, to receive therefrom the analytical model output, to determine, in response to the analytical model output, updated application parameter values, and to transmit the updated application parameter values to at least one application running on the digital computing system, for use by the application to set its application parameters, thereby to optimize execution of multiple applications running on the digital computing system, using updated runtime parameters. 
     The performance information can include performance information from any CPU, network or storage device in the digital computing system, and can be obtained, for example, by issuing a series of input/output commands to at least one element in the digital computing system. 
     In a further aspect of the invention, the performance profiling system is further operable to (a) continue to profile the performance of the storage system during operation, collecting a series of time-based samples, (b) transmit updated profiles to the analytical performance model system, and (c) enable the application parameter determination system to transmit updated sets of application parameters as the application executes. 
     The IRM can provide a selected degree of damping control over the Frequency of parameter modifications so that the system does not continually adapt to transient performance conditions. 
     In one practice or embodiment of the invention, the performance profiling system can communicate directly with individual elements of the digital computing system via a discovery interface. 
     The analytical performance model system can utilize queuing theory methods to determine a degree of load that the storage system can support, and the application parameter determination system can utilize the load values to determine parameter values for a given application. 
     The IRM can be configured so as to contain multiple parameter determination systems that can be allocated one per application; and the application parameter determination system can consider a range of application-specific parameters, including, for example, Cost-Based Optimization (CBO) parameters. 
     In addition, the analytical performance model system can be adjusted to determine and account for the impact of competing application workloads in an environment in which system resources are shared across multiple applications, and wherein a selected application can be favored. If multiple applications are sharing the same set of I/O storage resources, the application parameter determination system can adjust multiple sets of parameter values to facilitate improved resource sharing. Still further, the application parameter determination system can adjust parameter values to favor one application&#39;s I/O requests over another&#39;s. 
     The IRM of the present invention can be a discrete module in the digital computing system, or a module in any of a computing system subsystem or storage network fabric subsystem in the SAN. 
     Further details, examples, and embodiments are described in the following Detailed Description, to be read in conjunction with the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  (Prior Art) is a schematic diagram of a conventional workstation or PC (personal computer) digital computing system, on which the present invention may be implemented; or which may form a part of a networked digital computing system on which the present invention may be implemented. 
         FIG. 2A  (Prior Art) is a schematic diagram of a networked digital computing system on which the present invention may be implemented. 
         FIG. 2B  (Prior Art) is a schematic diagram of components of a conventional workstation or PC environment like that depicted in  FIG. 1 . 
         FIG. 3  is a schematic diagram of one embodiment of the present invention. 
         FIG. 4  is a schematic diagram of a digital computing system in which the present invention may be implemented. 
         FIG. 5  is a schematic diagram depicting an application program with adjustable application parameters. 
         FIG. 6  is schematic diagram of an application running on the digital computing system and generating a system load. 
         FIG. 7  is a schematic diagram depicting a computing system and an Information Resource Manager (IRM) constructed in accordance with the present invention. 
         FIG. 8  is a schematic diagram depicting a database of performance statistics, configuration data and application parameters for applications running on the computing system. 
         FIG. 9  is a schematic diagram depicting how performance information can be obtained, in accordance with the present invention, from the computing system. 
         FIG. 10  is a schematic diagram depicting how configuration information can be obtained, in accordance with the present invention, from each element of the computing system. 
         FIG. 11  is a schematic diagram depicting the analytical model aspect of the IRM, in accordance with one embodiment of the present invention. 
         FIG. 12  is a schematic diagram depicting how configuration data, CPU statistics, network statistics and SAN statistics can be used to construct the analytical model in accordance with the present invention. 
         FIG. 13  is a schematic diagram depicting how the analytical model generates an updated set of application parameters in accordance with one practice of the present invention. 
         FIG. 14  is a schematic diagram depicting how the updated application parameters are used to update the set of application parameters used by the application, in accordance with one practice of the present invention. 
         FIG. 15  is a schematic diagram depicting how the information resource manager (IRM) can maintain a number of CPU, network and SAN statistics. 
         FIG. 16  is a schematic diagram depicting how multiple sets of updated statistics can be used to drive an analytical model, which then updates the application data running on the computing system, in accordance with the present invention. 
         FIG. 17  is a schematic block diagram of the major components of the ELM architecture in accordance with one embodiment of the present invention. 
         FIG. 18  is a diagram depicting the timing of the collection of statistics for the ELM architecture. 
         FIG. 19  is a table providing a summary of the collection and calculation frequencies for the ELM statistics. 
         FIGS. 20-27  are a series of tables providing a summary of the ELM statistics. 
         FIG. 28  is a schematic diagram depicting various connectors contained in the EDaC service in accordance with one practice of the present invention —    
         FIGS. 29A ,  29 B and  30  are flowcharts showing various method aspects according to present invention for optimizing execution of multiple applications running on a digital computing system. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The following description set forth numerous specific details to provide an understanding of the invention. However, those skilled in the art will appreciate that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, protocols, algorithms, and circuits have not been described in detail so as not to obscure the invention. The following discussion describes various aspects of the invention, including those related to addressing load on storage resources, and aspects related to balancing CPU, network and SAN resources by properly adjusting application parameters. 
     Digital Processing Environment in Which the Invention can be Implemented 
     Before describing particular examples and embodiments of the invention, the following is a discussion, to be read in connection with FIGS.  1  and  2 A-B, of underlying digital processing structures and environments in which the invention may be implemented and practiced. 
     It will be understood by those skilled in the art that the present invention provides methods, systems, devices and computer program products that enable more efficient application execution on applications commonly found on compute server class systems. These applications include database, web-server and email-server applications. These applications are commonly used to support a medium to large group of computer users simultaneously. These applications provide coherent and organized access and sharing by multiple users to a shared set of data. The applications can be hosted on multiple or a single shared set of digital computing systems. The set of tasks carried out on each application dictates the patterns and loads generated on the digital computing system, which can be managed through a set of configurable application parameters. 
     The present invention can thus be implemented as a separate software application, part of the computer system operating system software or as dedicated computer hardware of a computer that forms part of the digital computing system. The present invention may be implemented as a separate, stand-alone software-based or hardware-based system. The implementation may include user interface elements such as a keyboard and/or mouse, memory, storage, and other conventional user-interface components. While conventional components of such kind are well known to those skilled in the art, and thus need not be described in great detail herein, the following overview indicates how the present invention can be implemented in conjunction with such components in a digital computer system. 
     More, particularly, those skilled in the art will understand that the present invention can be utilized in the profiling and analysis of digital computer system performance and application tuning. The techniques described herein can be practiced as part of a digital computer system, in which performance data is periodically collected and analyzed adaptively. The data can further be used as input to an analytical model that can be used to project the impact of modifying the current system. The applications running on the digital computer system can then be reconfigured to improve performance. 
     The following detailed description illustrates examples of methods, structures, systems, and computer software products in accordance with these techniques. It will be understood by those skilled in the art that the described methods and systems can be implemented in software, hardware, or a combination of software and hardware, using conventional computer apparatus such as a personal computer (PC) or an equivalent device operating in accordance with (or emulating) a conventional operating system such as Microsoft Windows, Linux, or Unix, either in a standalone configuration or across a network. The various processing aspects and means described herein may therefore be implemented in the software and/or hardware elements of a properly configured digital processing device or network of devices. Processing may be performed sequentially or in parallel, and may be implemented using special purpose or re-configurable hardware. 
     As an example,  FIG. 1  attached hereto depicts an illustrative computer system  10  that can run server-class applications such as databases and mail-servers. With reference to FIG. I, the computer system  10  in one embodiment includes a processor module  11  and operator interface elements comprising operator input components such as a keyboard  12 A and/or a mouse  12 B (or digitizing tablet or other analogous element(s), generally identified as operator input element(s)  12 ) and an operator output element such as a video display device  13 . The illustrative computer system  10  can be of a conventional stored-program computer architecture. The processor module  11  can include, for example, one or more processor, memory and mass storage devices, such as disk and/or tape storage elements (not separately shown), which perform processing and storage operations in connection with digital data provided thereto. The operator input element(s)  12  can be provided to permit an operator to input information for processing. The video display device  13  can be provided to display output information generated by the processor module  11  on a screen  14  to the operator, including data that the operator may input for processing, information that the operator may input to control processing, as well as information generated during processing. The processor module  11  can generate information for display by the video display device  13  using a so-called “graphical user interface” (“GUI”), in which information for various applications programs is displayed using various “windows.” 
     The terms “memory”, “storage” and “disk storage devices” can encompass any computer readable medium, such as a computer hard disk, computer floppy disk, computer-readable flash drive, computer-readable RAM or ROM element or any other known means of encoding digital information. The term “applications programs”, “applications”, “programs”, “computer program product” or “computer software product” can encompass any computer program product consisting of computer-readable programs instructions encoded and/or stored on a computer readable medium, whether that medium is fixed or removable, permanent or erasable, or otherwise. As noted, for example, in block  122  of the schematic block diagram of  FIG. 2B , applications and data can be stored on a disk, in RAM, ROM, on other removable or fixed storage, whether internal or external, and can be downloaded or uploaded, in accordance with practices and techniques well known in the art. As will also be noted in this document, the present invention can take the form of software or a computer program product stored on a computer-readable medium, or it can be in the form of computer program code that can be uploaded or downloaded, or fixed in an FPGA, ROM or other electronic structure, or it can take the form of a method or a system for carrying out such a method. Although the computer system  10  is shown as comprising particular components, such as the keyboard  12 A and mouse  12 B for receiving input information from an operator, and a video display device  13  for displaying output information to the operator, it will be appreciated that the computer system  10  may include a variety of components in addition to or instead of those depicted in  FIG. 1 . 
     In addition, the processor module  11  can include one or more network ports, generally identified by reference numeral  14 , which are connected to communication links which connect the computer system  10  in a computer network. The network ports enable the computer system  10  to transmit information to, and receive information from, other computer systems and other devices in the network. In a typical network organized according to, for example, the client-server paradigm, certain computer systems in the network are designated as servers, which store data and programs (generally, “information”) for processing by the other, client computer systems, thereby to enable the client computer systems to conveniently share the information. A client computer system which needs access to information maintained by a particular server will enable the server to download the information to it over the network. After processing the data, the client computer system may also return the processed data to the server for storage. In addition to computer systems (including the above-described servers and clients), a network may also include, for example, printers and facsimile devices, digital audio or video storage and distribution devices, and the like, which may be shared among the various computer systems connected in the network. The communication links interconnecting the computer systems in the network may, as is conventional, comprise any convenient information-carrying medium, including wires, optical fibers or other media for carrying signals among the computer systems. Computer systems transfer information over the network by means of messages transferred over the communication links, with each message including information and an identifier identifying the device to receive the message. 
     In addition to the computer system  10  shown in the drawings, methods, devices or software products in accordance with the present invention can operate on any of a wide range of conventional computing devices and systems, such as those depicted by way of example in  FIGS. 2A and 2B  (e.g., network system  100 ), whether standalone, networked, portable or fixed, including conventional PCs  102 , laptops  104 , handheld or mobile computers  106 , or across the Internet or other networks  108 , which may in turn include servers  110  and storage  112 . 
     In line with conventional computer software and hardware practice, a software application configured in accordance with the invention can operate within, e.g., a PC  102  like that shown in FIGS.  1  and  2 A-B, in which program instructions can be read from ROM or CD ROM  116  ( FIG. 2B ), magnetic disk or other storage  120  and loaded into RAM  114  for execution by CPU  118 . Data can be input into the system via any known device or means, including a conventional keyboard, scanner, mouse, digitizing tablet, or other elements  103 . As shown in  FIG. 2B , the depicted storage  120  includes removable storage. As further shown in  FIG. 2B , applications and data  122  can be located on some or all of fixed or removable storage or ROM, or downloaded. 
     Those skilled in the art will understand that the method aspects of the invention described herein can be executed in hardware elements, such as a Field-Programmable Gate Array (FPGA) or an Application-Specific Integrated Circuit (ASIC) constructed specifically to carry out the processes described herein, using ASIC construction techniques known to ASIC manufacturers. The actual semiconductor elements of a conventional ASIC or equivalent integrated circuit or other conventional hardware elements that can be used to carry out the invention are not part of the present invention, and will not be discussed in detail herein. 
     Those skilled in the art will also understand that ASICs or other conventional integrated circuit or semiconductor elements can be implemented in such a manner, using the teachings of the present invention as described in greater detail herein, to carry out the methods of the present invention as shown, for example, in FIGS:  3  et seq., discussed in greater detail below. 
     Those skilled in the art will also understand that method aspects of the present invention can be carried out within commercially available digital processing systems, such as workstations and personal computers (PCs), operating under the collective command of the workstation or PC&#39;s operating system and a computer program product configured in accordance with the present invention. The term “computer program product” can encompass any set of computer-readable programs instructions encoded on a computer readable medium. A computer readable medium can encompass any form of computer readable element, including, but not limited to, a computer hard disk, computer floppy disk, computer-readable flash drive, computer-readable RAM or ROM element, or any other known means of encoding, storing or providing digital information, whether local to or remote from the workstation, PC or other digital processing device or system. Various forms of computer readable elements and media are well known in the computing arts, and their selection is left to the implementer. 
     EMBODIMENTS OF THE INVENTION 
     There are now described particular examples and embodiments of the invention. 
     Instead of allocating disks or bandwidth to individual servers or applications, the systems and techniques described herein utilize the internal tuning facilities provided by an application, and arrive at tuned set of parameters based on the characteristics of the storage subsystem provided. Further, the present invention can also consider the resources of a complete digital computer system, such as a networked digital computing system. The described systems and techniques make use of existing performance monitoring systems and techniques that have been developed in commercial operating systems, such as Microsoft Windows, Linux and Unix. The described systems and techniques make use of existing interfaces to key database and email applications that enable adaptively tuning the application through a set of runtime parameters. The invention can further manage multiple applications concurrently, providing QoS guarantees through a careful provisioning of the available system resources. 
     Previous methods used to configure the application parameters that determine system performance suffer from a number of significant shortcomings: (1) tuning methods used to date have been based on trial-and-error iterative tuning, (2) users have had little information about the underlying CPU, network and storage subsystem to guide their tuning choices, (3) there has been little consideration given to managing multiple applications or multiple servers concurrently that utilize a shared digital computing system, and (4) there is presently no accepted methodology for translating the characteristics of a digital computing system to changes in individual application parameters. 
     Some applications are sensitive to the latency of storage access operations while others are not. Database and mail-server applications are particularly sensitive to the latency associated with storage access operations because they often access data in non-sequential modes and must sometimes await the completion of an access, or series of accesses, before issuing another command. 
     Many latency-sensitive applications, such as database systems, mail servers, and the like, have the ability to perform self-tuning. For instance, Oracle10g provides a query optimizer that can accelerate the performance of future queries based on the behavior of recent queries. Also, Oracle10g has over  250  tunable parameters that can affect database performance. These parameters can affect both the utilization of memory resources, e.g., caches and buffers, as well as define the amount of concurrent access possible, e.g., threading. 
     The described systems and techniques target the proper setting of these internal parameters by utilizing information about the underlying CPU, network and storage subsystems. As described herein, the CPU subsystem information includes both the type and number of processors being used, along with their associated memory hierarchy, the network subsystem information includes the speed and configuration of the network switch used and the speed of the adapters connected to the switch, and the storage subsystem information includes the characteristics of the physical disk devices, the grouping of these devices into RAID groups, the mapping of logical addresses to RAID groups, and the throughput of individual paths through this system. A further aspect of the invention provides the capability to obtain storage subsystem information by capturing runtime characteristics of the system. This information can be obtained by running customized exercisers or by observing the normal execution of the system. 
     The tuning of the application parameters may be done either upon initialization of the application, or dynamically. The methods used to capture the different characteristics of the underlying subsystem performance can be static, i.e., predetermined and shipped with the storage system, or acquired dynamically through profiling. The presently described invention includes methods to both specify this information statically, and obtain this information through profiling. According to a further aspect of the invention, this information is provided as feedback to an application to allow system parameters to be adjusted automatically *or by a system/application administrator. 
     The above discussion describes the need to properly adjust the parameters of performance-sensitive applications in order to make best use of the digital computing resources. An embodiment of an apparatus and system for adjusting such parameters is shown in  FIG. 3 . 
     As shown in  FIG. 3 , application servers  290  access a variety of storage elements, some directly connected to the servers  260 , and some connected to the servers via a storage area network  270  using a switch fabric  250 . This is just one possible organization of servers and storage systems. The present invention does not require a particular organization. 
     Accordingly to the presently described aspect of the invention, an element is introduced that can communicate with both the servers and the storage system. This element is referred to herein as the Storage System Aware Application Tuning System (SSAATS)  280 . This element and like structures and functions are also described and referred to below as the Information Resource Management (IRM) system. As described below, further aspects of the invention provide other named elements that perform some or all of the functions of the SSAATS element. 
     The embodiment of SSAATS shown in  FIG. 3  contains three sub-elements: 
     (1) the storage network profiling system  210 , 
     (2) an analytical model  220 , and 
     (3) the application parameter determination subsystem  230 . 
     The SSAATS element  280  can be implemented as a stand-alone subsystem, or can be integrated as part of the server subsystem  290  or the network fabric subsystem  240 . 
     The profiling subsystem element  100  has the ability to determine the degree of parallelism in the storage network, and can deduce the bandwidth and latency values for the underlying storage system  260  and  270  as discussed above. The profiling subsystem element  210  can also determine the bandwidth and latency values for the network fabric elements  250  present. 
     The profiling subsystem element  210  obtains performance-related information that is not always available from the storage system manufacturer. When a storage system is installed, the available storage can be configured in many different organizations. Thus, even if some performance-related information is provided by the manufacturer, the majority of the information that is needed is only relevant after the storage system has been installed and configured. 
     The necessary performance-related information includes, for example, but is not limited to: 
     (1) the degree of parallelism that is available in the CPU, network, and SAN, 
     (2) the speed of the various devices, 
     (3) the bandwidth of the paths between the application server, the network and the individual storage devices, and 
     (4) the configuration of the storage devices as viewed from the server. 
     To obtain the necessary performance-related information, a series of input/output commands can be issued to the storage subsystem. Based on the response time and throughput of particular command sequences, the necessary performance-related information can be obtained. This information is then fed to the analytical model element  220 . 
     The analytical model element  220  obtains profile information from the profiling storage network  210 . The profiling data is consumed by an analytical performance model  220  that is used to establish the appropriate loads that the CPU subsystem on the application server  290 , the network subsystem  250 , and the storage subsystem  260  and  270  can sustain. The output of the analytical model element  220  is fed to the element that determines the parameter values  230 , which then communicates these values to the application servers  290 , which in turn will set internal parameters in the application. 
     An optional embodiment is to allow the profiling system to continue to profile the performance of storage system through the profiling network  210 , to feed dynamic profiles to the analytical performance model  220 , and to communicate a new set of application parameters from the parameter determination system  230  to the application servers  290 . Key features of this optional embodiment include: (a) the profiling system cannot introduce significant overhead into the digital computing system, which might reduce the benefits obtained through parameter modifications, and (b) the system must make sure that appropriate control is provided to throttle the frequency of parameter modifications so that the system does not continually adapt to performance transients. 
     An optional embodiment is to allow the profiling system  210  to communicate directly with the storage resources  260  and  270  through a network interface, referred to herein as “Discovery,” in order to further refine the usage of the available system configuration. 
     The analytical model  220  described herein utilizes standard queuing theory techniques, and establishes how much load the storage subsystem can support. In particular, analytical model  220  can apply known queuing theory equations, algorithms and techniques to determine a supportable storage load. Such equations, algorithms and techniques are described, by way of example, in Kleinrock, L.,  Queueing Systems: Volume I—Theory  (Wiley Interscience, New York, 1975); Kleinrock, L.,  Queueing Systems: Volume II—Computer Applications  (Wiley Interscience, New York, 1976), both incorporated herein by reference as if set forth in their entireties herein. The parameter determination element then translates these load values into the specific parameter values of the target application. According to a further aspect of the invention, the SSAATS  280  contains multiple parameter determination elements  230 , one per application software. 
     The determination of application parameters unit  230  will consider a range of application-specific parameters. One particular set of parameters includes, for example, the Cost-Based Optimization (CBO) parameters provided inside of Oracle 10g. These parameters can control how indexing and scanning are performed within Oracle, as well as the degree of parallelism assumed by the application. For example, the multi-block read count can be set to adjust the access size or set parallel automatic tuning to run parallelized table scans. 
     In many situations, it may be beneficial for a storage administrator to segregate applications by latency sensitivity. While the presently described mechanism is targeted to throttle an individual application&#39;s system resource requests, since the network and storage is commonly shared across different applications, the same system can be used to manage multiple applications. 
     If network and storage is shared across different applications, the analytical model  220  can be adjusted to capture the impact of competing application workloads. Two typical workloads would be an online transaction processing workload competing with a storage backup workload. While the backup application is performing critical operations, execution should favor the online transaction processing application. 
     If multiple applications are sharing the same set of  10  storage resources  260  and  270 , then the determination of application parameters unit  230  will need to adjust multiple sets of parameter values to facilitate sharing. 
     When multiple applications share the same set of  10  storage resources  260  and  270 , and if the user of system administrator desires to prioritize the throughput of each application, the determination of application parameters unit  230  can further adjust parameter values to favor one application&#39;s  10  requests over another. 
     There is now described a further embodiment of a system according to the present invention, in which the above-described elements and others are described in greater detail. 
       FIG. 4  is a diagram illustrating elements of an exemplary computing system  300 , including central processing units (CPUs)  301 ,  302 ,  303 , a network element  310  and a storage array network  320 . The depicted configuration is typical of many currently available server-class computing systems. As described herein, aspects of the present invention are directed to systems and techniques for improving the performance of system  300  by constructing an analytical model of system  300 . The analytical model is constructed by first obtaining system configuration information and runtime performance statistics of the different elements. The analytical model is provided with knowledge with respect to the particular set of applications running on system  300 . The output of the analytical model includes performance numbers, as well as recommendations as to how to adjust the application parameters associated with the applications running on the computing system  300 . The output of the analytical model can then be used to improve the future performance of the applications. 
       FIG. 5  shows a diagram of an application  350 , which includes program code  360  and a set of application parameters  370  that are used to configure how the application  350  will run on computing system  300 . 
       FIG. 6  shows a diagram of an application  350 , which runs on CPU  1301 , which is supplied with a set of application parameters  370 , generating a load on the system. 
       FIG. 7  shows a diagram illustrating computing system  300  and an information resource manager  400 . The information resource manager  400  contains an analytical model  410  and maintains a database  420  of a number of computing system performance statistics  430 , including CPU statistics  440 , network statistics  450 , and SAN statistics  460 , computing system configuration data  470 , and the set of application parameters  370  for the set of applications running on the computing system  370 . 
       FIG. 8  shows the database  420  of CPU statistics  440 , network statistics  450 , SAN statistics  460 , configuration data  470 , and the application parameters  370  for the applications running on computing system  300 . 
       FIG. 9  shows a diagram illustrating an example of how performance statistics can be obtained from the computing system  300 . CPU statistics  440  can be obtained from CPU  1   301  using standard&#39;software utilities such as iostat  510  and perfmon  520 . Network statistics  450  can be obtained using the SNMP interface  530  that is provided on most network switch devices. SAN statistics  460  can be obtained via SMIS  540 , which is provided on many SAN systems  120 . The interfaces shown in  FIG. 9  show one particular set of interfaces for obtaining performance statistics from the different elements, but does not preclude the information resource management unit  400  from accessing additional interfaces on the computing system that are available. 
       FIG. 10  shows how configuration data  410  is obtained from each element of the computing system  100 . Each vendor of the different computing system elements  100  generally provides an interface to report this information. 
       FIG. 11  shows a diagram of analytical model  410 , which is part of the information resource management unit  400 . The purpose of the analytical model  410  is to both generate performance indicators and-produce an updated set of application parameters  372  ( FIGS. 13-14 ) in order to improve the performance of applications running on the computing system  300 . 
       FIG. 12  shows how the configuration data  470 , along with the CPU statistics  430 , network statistics  430  and SAN statistics  440 , are used to construct the analytical model  410 . The analytical model contains models of the CPUs  411 , network  412 , and SAN  413 , and may also contain additional computing system elements. 
       FIG. 13  shows how the analytical model  410  generates an updated set of application parameters  372 . This new set of parameters will be fed to the computing system to reconfigure how the applications  350  running on the system use the elements of the computing system. The goal is to improve performance of the system. 
       FIG. 14  shows how the updated application parameters  372  are used to update the set of application parameters  370  used by the application  350 . While  FIG. 14  shows that the application is running on CPU  1   301 , the application could run on any CPU on the system  302 ,  303 , or on any other element in the system network  310  or SAN  320 . 
       FIG. 15  shows that the information resource management unit can maintain a number of CPU  442 , network  452  and SAN  462  statistics. These records are typically time-ordered and provide longer-term behavior of the system. This set of records can also represent performance statistics produced for multiple applications running on the computing system. This richer set of statistics can again to drive an analytical model  410 , which then updates the application data  372  running on the computing system. This technique is further illustrated in  FIG. 16 . 
     ADDITIONAL IMPLEMENTATION DETAILS/EXAMPLES 
     The following discussion provides additional detail regarding one or more examples of implementations according to various aspects of the present invention. It will be understood by those skilled in the art that the following is presented solely by way of example, and the present invention can be practiced and implemented in different configurations and embodiments, without necessarily requiring the particular structures described below. The following discussion is organized into the following subsections:
         1. System Architecture   2. The External Discovery Subsystem   3. Discovery Engine       

     1. System Architecture 
     The presently described architecture is generally referred to herein as Event Level Monitor (ELM). The ELM architecture supports the following ELM product features: (1) data center visibility; (2) hot spot detection; and (3) analysis. 
     In order to support these capabilities the ELM architecture provides the following features: configuration/topology discovery; statistics gathering; statistics calculations; application-specific storage topology and statistics; analysis; and alarm and event generation. 
       FIG. 17  shows a block diagram of the major components of an exemplary embodiment of the ELM architecture  600 . Each of the depicted components is now described in turn. 
     Platform  610 : The platform  610  provides the foundation upon which and the basic environment in which the IRM  400  runs. 
     Linux  620 : The Linux OS  620  provides the low level functions for the platform. 
     Component Task Framework (CTF)  630 : The Component Task Framework  630  provides a useful set of common primitives and services, messaging; events; memory management; logging and tracing, debug shell; timers; synchronization; data manipulation, including hash tables, lists, and the like. 
     MySQL  640 : The repository of the system&#39;s data, the Data Store (DS)  650 , is stored in a centralized database built on top of MySQL  640 . 
     Data Store (DS)  650 : The DS  650  contains the discovered elements, their relationships or topology, and their statistics. 
     Information Resource Manager (IRM)  400 : The Information Resource Manager (IRM)  400 , discussed above, is responsible for collecting all the information, topology and statistics, about the data center. 
     External Discovery and Collection (EDaC)  700 : The External Discovery and Collection (EDaC)  700 , described further below, component provides the system with its connection to the elements, such as servers and storage arrays, of the data center. It knows how to talk to each specific type of element, e.g. CLARiiON storage array, and discover its topology or gather statistics from it. Thus, it has separate modules, or collectors, for each specific array or server. There is a standard API for each type of element which is defined in XML and which every collector conforms to. 
     Discovery Engine  660 : The Discovery Engine  660  drives the discovery of the topology of the data center elements, specifically servers and storage arrays. The user enters the servers and storage arrays that he wants discovered. The Discovery Engine  660  accesses the Data Store  650  to get the lists of servers, networks, and storage arrays the user has entered. For each one, the Discovery Engine  660  asks the EDaC  700  to get its topology. The EDaC  700  queries the elements and returns all the information discovered, e.g. disks for storage arrays. The Discovery Engine  660  then places this information in the Data Store  650  and makes the relationship connections between them. On the first discovery for a server, the Discovery Engine  660  also notifies the Statistics Manager  670  to begin collecting statistics from the server. In addition, the Discovery Engine  660  also periodically wakes up and “re-discovers” the elements of the digital computing system  300 . This allows any topology changes to be discovered. 
     Statistics Manager  670 : The Statistics Manager  670  drives the gathering of statistics from computer system elements, specifically servers. In the current product, statistics are only gathered from servers, although these statistics are used to derive statistics on other data center elements as well. The Statistics Manager  670  is notified by the Discovery Engine  660  when a new server has been discovered. It then adds the server to its collection list. Periodically it wakes up and runs through its collection list. For each server in the collection list, it asks the EDaC  700  to collect the statistics for it. Once the EDaC  700  has collected the statistics for a server it sends these to the Statistics Manager  670 . The Statistics Manager  670  processes these statistics and inserts them into the Data Store  650 . Some statistics are added to the Data Store  650  unmodified, some are added after some simple processing, such as averaging, and others are processed with more sophisticated algorithms which derive completely new statistics. 
     Statistics Monitor  680 : New statistics are constantly being gathered and calculated. This means that a user can go back in time to see what was happening in the system. All statistics are stored in the Data Store (DS)  650 . The stored statistics include calculated as well as gathered statistics. This makes them always immediately available for display. 
     The Statistics Monitor  680  monitors and manages statistics once they have been put into the Data Store  650  by the Statistics Manger  670 . Inside the Statistics Monitor  680  are several daemons that periodically wake up to perform different tasks on the statistics in the Data Store  650 . These tasks include: creating summary statistics, for instance rolling up collected statistics into hourly statistics; calculate moving averages of some statistics; compare some statistics against threshold values and generate events, which eventually generate alarms when thresholds are crossed. 
     There are different types of statistics calculated and analyzed. Some of these include the following: 
     Calculated Statistics: Calculated statistics are statistics that are created by performing calculations on gathered or other calculated statistics. The calculations can be as simple as a summation or as complicated as performing a non-linear curve fit. They are stored in the DS  650  in the same way and format as the statistics that are gathered. 
     Calculated Storage Statistics: It is important to note that all storage statistics are derived from the statistics gathered from Server LUNs. The discovered Server and Storage Array Topologies are then used to derive the statistics for the other storage objects: Server Volume, Storage Array LUN, ASG, and Sub-Group. 
     Collection and Calculation Frequencies: Statistics collection is done in a manner such that utilization can be calculated over a time when the system is statically stable. Statistically stable does not mean that the statistics are unchanging, but rather that the system is doing the same type of work; or set of work, over the period. Calculating utilization requires a series of samples. Thus, in order to calculate utilization on a statistically stable period a series of samples must be collected in a short period of time. However, constantly collecting statistics at a high frequency for a significant number of servers puts too high a burden on the system. The above requirements/restraints are met by collecting statistics in bursts, as shown in  FIG. 18 . 
     The parameters have the following meanings: 
     Major Period 
     The time between bursts of samples. The range is 5 to 60 minutes. 
     Minor Period 
     The time between each sample of a burst. The range is 1 to 10 seconds. 
     Burst 
     The number of samples taken each major period at the minor period rate. The range is 1 to 50 samples. 
     These parameters are variable on a per server basis. Thus it is possible to collect statistics on one server with a major period of 30 minutes, minor period of 10 seconds and a burst size of 10, while collecting statistics on another server with a major period of 15 minutes, minor period of 1 second and a burst size of 25. Statistics that are not used in calculating utilization are collected once at the major period frequency. Statistics collected in a burst are used immediately to calculate utilization. The result of the utilization calculation is saved in the DS and the raw data is discarded. Thus, statistics are inserted into the DS once per major period per server. 
     Server Statistics Calculation Frequency: All the statistics for a server: CPU, memory, LUNs and Volumes, are collected and calculated at the same time. This is done at the major sample rate for the server. 
     ApplicationStorageGroup/StorageGroup Statistics Calculation Frequency: A particular issue is the calculation period for ApplicationStorageGroups (ASGs) and StorageGroups (SGs). The statistics for ASGs and SGs are calculated from Server LUN statistics that could come from different servers. Most likely these Server LUN statistics are collected at different times and also at potentially different rates. This means that the ASG/SG statistics cannot be calculated at a Major Sample Period. They must be calculated at some slower rate, so that multiple samples from each Server LUN can be used. 
     Current Status Update Frequency: Many objects keep a current, historic and trend status. The current status is calculated relatively frequently, but slower than Major Sample rate. 
     Historic Status and Trend Update Frequency: The historic status and trend are longer term indicators and are thus calculated less frequently. 
     Summary Calculation Frequency: Summarization is a mechanism by which space is saved in the database. It operates under the theory that older data is less valuable and does not need to be viewed at the granularity as newer data. 
     Discovery Frequency: Discovery gathers relatively static data about the environment. As such, it does not need to run very often. However, this needs to be balanced with desire for any changes to appear quickly. 
     Summary of Collection and Calculation Frequencies: The table shown in  FIG. 19  provides a summary of the collection and calculation frequencies. Note that all collection and calculation parameters should be parameterized so that they can be modified. 
     Statistics Summary: The tables shown in  FIGS. 20-27  provide a summary of the statistics for the ELM system described herein. 
     FIG.  20 —Server Statistics Collected 
     Server statistics are gathered from the server. These are dynamic statistics that are gathered frequently at the Major Sample Period rate. 
     FIG.  21 —Server Attributes Collected 
     Server attributes are gathered from the server. These are relatively static parameters that are gathered infrequently at the Discovery rate. 
     FIG.  22 —Server Attributes Stored 
     Server attributes are gathered from the server. These are relatively static parameters that are gathered infrequently at the Discovery rate. 
     FIG.  23 —Server Current Statistics Stored 
     Server statistics are generated from the collected server statistics and then stored in the database. There should be one of these generated per. Major Sample Period per server. 
     FIG.  24 —Server Summary Statistics 
     Summary server statistics are rollups of server statistics from a shorter time period to a longer time period. For instance, major period statistics can be summarized into daily or weekly statistics. 
     FIG.  25 —Storage Statistics Stored 
     There is a common storage statistic that is used to store statistics for a variety of storage objects. The frequency with which a storage statistic is generated depends on the object it is being generate for. Server Volumes—one per major sample period; Server LUNs—one per major sample period; Application Storage Groups—one per Application Storage Group/Storage Group calculation period; Sub-Groups—one per Application Storage Group/Storage Group calculation period. 
     FIG.  26 —Storage Statistics Stored 
     Not every statistic is valid for every object. The  FIG. 26  table shows which statistics are valid for which objects. 
     FIG.  27 —Summary Storage Statistics Stored 
     Summary storage statistics are rollups of storage statistics from a shorter time period to a longer time period. For instance, major period statistics can be summarized into daily or weekly statistics. 
     Analysis: Analysis uses the data stored in the Data Store, primarily topology and statistics, to inform the user about what is happening to his system, or to make recommendations for the system. The analyses can either be implemented as a set of rules that are run by the rules engine against the data in the Data Store, or as an analytical model that be used to adjust application parameters. There are several different types of analysis that can be run. These include the following: 
     Application Point In Time Analysis 
     Analyzes what is going on with an application&#39;s performance and its use of resources at a point in time. 
     Application Delta Time Analysis 
     Analyzes what has changed with an application&#39;s performance and its use of resources between two points in time. 
     Application Storage Group Analysis 
     Analyzes a path between the application and the storage at a point in time to determine whether it is a hot spot and whether there is application contention for it. 
     Storage Provisioning Recommendation 
     Makes a recommendation as to where to provision more physical storage for an application. 
     Application Recommendations 
     Make modifications to the application parameters. 
     In addition to the foregoing, those skilled in the art will understand that various APIs (Application Programming Interfaces), constructed in accordance with known API practice, may be provided at various points and layers to supply interfaces as desired by system designers, administrators or others. 
     2. External Discovery and Collection Service 
     There is now described in greater detail the above-mentioned External Discovery and Collection (EDaC) service, which provides access to all configuration and statistics for resources external to the appliance. The EDaC service is responsible for dispatching requests to any external resource.  FIG. 28  is a diagram illustrating the variety of connectors contained in an exemplary embodiment of the EDaC service  700 . Each connector  730  provides access to a specific resource. 
     The list of responsibilities includes the following: (1) listen for statistics request events, and forward them to the appropriate connectors; (2) listen for discovery request events, and forward them to the appropriate connectors; and (3) perform discovery requests on all connectors on some schedule, and generate discovery events. According to a further aspect of the invention, the functionality of item (3) may be moved to the Information Resource Manager (IRM). 
     There are two parts to the discovery process: (1) “finding” a device, and (2) figuring out the mostly static configuration for the device. The discovery algorithms must be robust enough to handle thousands of devices. A full discovery process may take hours. With respect to configuration, the following data is needed in the object model to accomplish discovery and collection: 
     Server: IP address, login/password; SSH/telnet, if Solaris; polling interval; and persistent connection. 
     StorageArray: management server; login/password; path to CLI; polling interval; persistent connection. 
     Application: IP address, login/password; service name, port; polling interval; persistent connection. 
     Various well-known data access tools can be utilized in conjunction with this aspect of the invention, and multiple access methods, including configurable access methods, may be employed. These could include telnet access to a server, database data access via ODBC (which may utilize ODBC libraries commercially available from DataDirect Technologies of Bedford, Mass.), SSH techniques, and other conventional techniques. 
     Sequenced Event Broker  710  provides an interface to the EDaC Core  720 , which contains the described Connectors  730 . 
     The Oracle Database Connector  730   a  is responsible for collecting the database configuration and database statistics. Oracle Database Connector  730   a  uses the ODBC library  740 . 
     The Windows and Solaris Server Connectors  730   b  and  730   c  are responsible for collecting OS-level data, such as memory utilization, and Volume/LUN mappings and statistics. In order to calculate Volume/LUN mappings, it may be necessary to understand both the installed volume manager as well as the multipathing product. Even if it is not necessary to understand the specifics of each, i.e. striping characteristics or path info, it is likely that info will be needed from each product just to calculate which LUNs are associated with the volume. Specific products may be picked to target for Elm. The Solaris Server Connector  730   c  uses SSH. The volume managers for Solaris are Veritas and the native one. The Windows Server Connector  730   b  uses the WMI library  750 . The volume manager for Windows is the native one, which is Veritas. 
     The Storage Connectors  730   d ,  730   e  and  730   f  are responsible for collecting LUN utilization, performance, and mapping to raid sets/disks, and other data generally represented by box  760 . No array performance statistics are needed for ELM. 
     With respect to the CLARiiON Storage Connector  730   d , NaviCLI is a rich CLI interface to the CLARiiON. It can return data in xml. Performance statistics can be enabled on the CLARiiON and retrieved through the CLI. It would also be possible to install the CLI on the ASC. It is more likely that the CLI would be accessed from one of the customer servers through SSH  780 . Some data is also available by telnet directly to the CLARiiON. 
     With respect to the Dothill Storage Connector  730   e , the Dothill also has a host-based CLI. It can return data in xml. The Dothill provides no access to performance statistics. The access issues are the same as with the CLARiiON CLI. Some data is also available by telnet directly to the Dothill. 
     A suitable HP Storage Connector  730   f  is also provided. 
     As represented by box  730   g , the presently described system may be modified and expanded to include the following elements: CIM/WBEM/SMI-S access; SNMP access; fabric connectors; external SRM connector; remote proxies/agents; events to change configuration. Further, one Windows agent may serve as gateway to “the Windows world,” and would integrate with WMI and ODBC more seamlessly. These future access tools are represented by box  770 . 
     3. Discovery Engine 
     The above-mentioned Discovery Engine is now described in greater detail. The Discovery Engine (DE) resides in the Information Resource Manager (IRM). It is responsible for initiating periodic topology discovery of servers and storage arrays that have been entered into the Data Store (DS) by the user. It does this in conjunction with the External Discovery and Collection (EDaC) module, described above. 
     The DE is built around a main loop that processes messages from its message queue. These messages include: 
     Discovery Timer Event 
     This event initiates a full discovery process. 
     Discovery Complete Events 
     These are the Discover Storage Array Topology and Discover Server Topology events that were originally sent to the EDaC by the DE, and are now being returned by the EDaC after the EDaC has generated all the discovery events for the server or storage array. These events indicate that the topology discovery has been completed for the server or storage array. 
     Object Discovery Events 
     The EDaC generates a discovery event for each object it discovers in the process of determining the topology of a server or storage array. For example, the EDaC generates Server, Server FC Port, Server Volume, and Server LUN discovery events when it is requested to determine the topology of a server. 
     The main loop can simply wait on the message queue for the next message to process. 
     Discovery Timer Event The DE uses the Component Task Framework (CTF) to set a discovery interval timer. When the timer has elapsed, the CTF generates a message and delivers it to the DE&#39;s message queue. This tells the DE that it is time to begin a discovery process. 
     The Discovery Timer event causes the DE to launch N initial Discover Server Topology or Discovery Storage Array Topology events in parallel. N is an arbitrary number. Until there are no more servers or storage arrays to discover topology on, there will always be N outstanding discover topology events. 
     Server or Storage Array Discovery Complete Event: A Server or Storage Array Discovery Complete event is actually a Discover Server Topology or Discover Storage Array Topology event that has been returned to DE once the EDaC has completed the discovery on that object. 
     Discovery Complete Event Processing: The processing steps are as follows: 
     1. The DE queries the DS to find out if any of the existing record, e.g. a server LUN that was not discovered during the objects topology discovery. It does this by creating a query for all records whose discovery timestamp is not the same as that of the current record. 
     2. For each record whose timestamp does not match, an lost event, e.g. Server Volume Lost event, is generated and sent. 
     3. If there are more servers or storage arrays to be discovered, then the next one is retrieved from the DS and a Discover Topology event is sent for it to the EDaC. 
     4. If there are no more servers or storage arrays to discover, then the discovery is complete and the discovery interval timer is restarted. 
     Object Discovery Event: On receipt of a Discover Topology event the EDaC queries the server or storage array for its topology. The topology consists of a set of records. The EDaC generates a set of discovery events for the current event. It is important that the discovery events occur in a certain order: 
     Server Topology Discovery Events: Server Discovery Event; Server FC Port Discovery Event(s); Server Volume Discovery Event(s); Server LUN Discovery Event(s). 
     Storage Array Topology Discovery Events: Storage Array Discovery Event; Storage Array FC Port Discovery Event(s); Storage Array Disk Discovery Event(s); Storage Array LUN Discovery Event(s). 
     Included in each discovery event is a timestamp for the discovery. The timestamp is inserted by the EDaC. Each discovery event for a particular storage array or server has the same timestamp value. 
     Discovery Processing: The processing steps are as follows: 
     1. The DE queries the Data Store to determine if the record already exists. 
     2. If the record already exists, then the records relationships are verified and the discovery timestamp is updated. 
     3. If the record does not exist in the DS, then it is created along with its relationships to other records. Thus, processing at this step is particular to the record being discovered. 
     4. A “record discovered” event is created and logged. 
     General Method 
       FIG. 29A  is a flowchart of a general method  1000  for optimizing execution of multiple applications running on the digital computing system. The method may advantageously be practiced in a networked digital computing system comprising at least one central processing unit (CPU), a network operable to enable the CPU to communication with other elements of the digital computing system, and a storage area network (SAN) comprising at least one storage device and operable to communicate with the at least one CPU. The computing system is operable to run at least one application program, the at least one application program having application parameters adjustable to control execution of the application program. 
     An exemplary method in accordance with the invention is illustrated in boxes  1001 - 1003 : 
     Box  1001 : utilizing an Information Resource Manager (IRM), operable to communicate with elements of the digital computing system to obtain performance information regarding operation of and resources available in the computing system, to communicate with the at least one CPU, network and SAN and obtain therefrom performance information and configuration information. As noted elsewhere in this document, performance and configuration information can be from any CPU, network or storage device in the digital computing system. Information can be obtained by issuing I/O or other commands to at least one element of the digital computing system. The IRM can be a discrete module in the digital computing system, or implemented as a module in a computing system subsystem or storage network fabric subsystem in the SAN. 
     Box  1002 : utilizing the performance information and configuration information to generate an analytical model output, the analytical model output comprising any of performance statistics and updated application parameters. As noted elsewhere in this document, the invention can utilize queuing theory to determine a degree of load the storage system or subsystem can support. 
     Box  1003 : utilizing the analytical model output to determine updated application parameter values, and to transmit the updated application parameter values to at least one application running on the digital computing system, for use by the application to set its application parameters, thereby to optimize execution of multiple applications running on the digital computing system, using updated runtime parameters. As noted elsewhere in this document, the method can utilize load values, e.g., the load values determined using queuing theory, to determine parameter values for a given application. The method can also involve the consideration of a range of application-specific parameters, e.g., Cost-Based Optimization (CBO) parameters, in determining updated application parameter values. 
       FIG. 29B  shows how the method  1000  of  FIG. 29A  can continue to run, iteratively or otherwise, including by continuing to profile the performance of the storage system during operation, thereby collecting a series of time-based samples ( 1004 ), generating updated profiles in response to the time-based samples ( 1005 ), and in response to the updated profiles, transmitting updated sets of application parameters as a given application executes ( 1006 ). As discussed elsewhere in this document, the method can include providing a selected degree of damping control over the frequency of application parameter updates, so that the system does not continually adapt to performance transients in performance conditions. The method can also include communicating directly with individual elements of the digital computing system via a discovery interface. (An exemplary correspondence between  FIG. 29B  and  FIG. 29A  is indicated via points “A” and “B” in the respective drawings.) 
       FIG. 30  shows how, in accordance with discussion elsewhere in this document, a method  1010  according to the invention can further be implemented in an environment in which multiple applications are sharing network, storage or other resources, including by adjusting the analytical model to determine and account for the impact of competing application workloads ( 1011 ), adjusting multiple sets of parameter values to facilitate improved resource sharing ( 1012 ), and adjusting parameter values to favor one application, or its I/O requests or other aspects, over another application, or its  1 / 0  requests or other aspects, if desired. 
     Conclusion 
     While the foregoing description includes details that will enable those skilled in the art to practice the invention, it should be recognized that the description is illustrative in nature and that many modifications and variations thereof will be apparent to those skilled in the art having the benefit of these teachings, and within the spirit and scope of the present invention. It is accordingly intended that the invention herein be defined solely by the claims appended hereto and that the claims be interpreted as broadly as permitted by the prior art.