Abstract:
A computing resource allocation system allocates hardware and software resources among employees, based upon a combination of the employee level, job function, and demonstrated workstation performance within the context of the job requirements of the employee and usage patterns of the computing resource. The system collects various performance data for computing resources. A set of policy rules is applied to the collected performance data and processed by the present system. Consequently, the present system automatically identifies and prioritizes employees in need of technology upgrades and replacements based on business needs and available resources. Performance data of a computing resource is captured and transmitted to a central collection agency. From the performance data, the present system determines when partial upgrades, such as memory additions or faster adapters are appropriate based on system performance or errors. In addition, the present system determines when a computing resource experiences continuous performance problems.

Description:
FIELD OF THE INVENTION  
       [0001]     The present invention generally relates to monitoring performance of computing resources such as computers. More specifically, the present invention pertains to applying policy rules to collected performance data to best allocate hardware and software resources among employees in an automatic fashion within the context of job requirements of an employee and usage patterns of a computing resource.  
       BACKGROUND OF THE INVENTION  
       [0002]     With most modern businesses relying on computing resources for business efficiency and competitive advantage, it is imperative that key employees have access to the type of computing equipment required to effectively execute their jobs. In today&#39;s information technology (IT) environment, keeping employee computer systems running at an optimal level is a challenge that occupies many people. The problems inherent in this challenge are that of either over-allocation or under-allocation of resources to specific employees.  
         [0003]     Over-allocation may be illustrated by the case where a “top of the line” new computer system is delivered to a new employee with little need for the hardware or software he or she has been assigned. By providing a cutting-edge system to an entry-level employee with need for little more than email functionality, the corporation or issuing entity has effectively wasted money and has mismanaged important business resources. In such over-allocation, the company may be delivering a $2,000 system to an employee whose needs may be met by a $500 system or through reuse of an older, “surplus” system.  
         [0004]     Under-allocation creates productivity problems as well as other more subtle problems. For example, the issuance of a low-end system to a customer-facing marketing executive creates a problem with perceived corporate competence by the customer. One can easily imagine a case where such an executive delivers a presentation before an important client and that client is unimpressed by the poor performance of the computer system and the delivery of the presentation. Consequently, under-allocation has the possible business impact of lost revenue and client goodwill. Similarly, a developer with significant computational needs may be assigned, an old, outdated system. In doing so, he or she may be forced to endure very long compile times or computational modeling times with a business impact of wasted productivity and delays to market.  
         [0005]     Keeping employee computer systems running in optimum condition also comprises trading available resources with a corporate budget. As employees experience difficulties with computers, the issue becomes whether to repair or replace the system. A further issue is the need to objectively prioritize computing resource needs to best maximize company performance and productivity. Currently, corporate policies regarding computing resource replacement require replacement based on life of the computing resource. However, an employee that is using a computer at full capacity may experience substantial performance degradation long before the replacement cycle provides a new computing resource. Conversely, an employee using a computing resource in a minimal fashion may be able to use the same computing resource longer than the replacement cycle indicates.  
         [0006]     Currently, performance issues are resolved when an employee contacts their manager or a corporate help desk and complains about poor system performance. An IT staff member then typically looks at the system and attempts to determine if a problem exists. After a lengthy process, a repair, upgrade, or replacement option may be pursued. This process can become very burdensome, particularly in environments where many users and computers exist. Meanwhile, the employee is experiencing a reduction in productivity while dealing with inadequate performance from their computer.  
         [0007]     What is therefore needed is a system, a service, a computer program product, and an associated method to monitor the performance of computation resources and objectively distribute important computing resources on the basis of computer resources need, job functions, and responsibilities. The need for such solution has heretofore remained unsatisfied.  
       SUMMARY OF THE INVENTION  
       [0008]     The present invention satisfies this need, and presents a system, a service, a computer program product, and an associated method (collectively referred to herein as “the system” or “the present system”) for best allocating hardware and software resources among employees based upon a combination of the employee level, job function, and computer performance, usage patterns, and computer performance need within the context of the job requirements of the employee. The present system collects various performance data for computing resources. A set of policy rules is applied to the collected performance data and processed by the present system. Consequently, the present system automatically identifies and prioritizes employees in need of technology upgrades and replacements based on business needs and available resources.  
         [0009]     The present system provides an objective and automated system whereby computer resources in need of upgrade or replacement are identified and prioritized based on business requirements. Performance data of a computing resource is captured and transmitted to a central collection server. From the performance data, the present system determines when partial upgrades such as, for example, memory additions or faster adapters are appropriate based on system performance. In addition, the present system determines when a computing resource experiences repetitive performance problems. These performance problems represent poor work productivity for the user and indicate that replacement of a computing resource is appropriate.  
         [0010]     The present system determines when error conditions are present in the computing resource that require partial upgrades, parts replacement, or replacement of the computing resource. Further, the present system applies policy rules to the collected performance data such that employees in critical roles, such as customer-facing positions, management positions, or other revenue-generating positions are given a higher priority for upgrades and system replacements as such resources become available.  
         [0011]     Priority may be assigned in different ways. For example, a heavier weight in a weighted system could be used. Alternatively, it is possible to assign a mandatory priority, such as, for example: this class of person will always go to the top of the list, as compared to the heavier weight model wherein a heavier weight in conjunction with low resource needs would not necessarily make that person a higher priority over a developer with greater need and large revenue generating missed opportunity.  
         [0012]     In addition, the present system applies policy rules to the collected performance data such that employees of equivalent rank or importance are assigned a priority for replacement of computer resources based on usage of the computer resource rather than an arbitrary replacement schedule such as, for example, every three years. Usage of the computing resource is measured, for example, by keyboard strokes or composite performance parameters or metrics.  
         [0013]     The present invention may be embodied in a utility program such as an automatic hardware allocation utility program. The present invention also provides means for the user to specify an optimum allocation of resources that meets budgetary constraints. The present invention further provides means to identify an optimum allocation of component and computer hardware replacements and upgrades based on an employee&#39;s job description and level.  
         [0014]     The present invention further provides means for the user to identify an optimum allocation of component and computer hardware replacements and upgrades based on a usage of a computing resource and available resources for providing those replacements and upgrades. Furthermore, the present invention provides means for the user to identify criteria or metrics which indicate a level at which a component or computing resource is replaced or upgraded. The criteria or metrics are tables correlating employee job description and level with allowed component and computer performance.  
         [0015]     The user may invoke the automatic hardware allocation utility to monitor computing resources, identify components and computing resources requiring replacement or upgrade, prioritize component and computing resource replacement or upgrade, and recommend resource allocation to employees that maximizes productivity with respect to the use of company resources. The set of requirements comprises a table of metrics correlating employee level and employee job description with allowed performance of the component or the computing resource.  
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0016]     The various features of the present invention and the manner of attaining them will be described in greater detail with reference to the following description, claims, and drawings, wherein reference numerals are reused, where appropriate, to indicate a correspondence between the referenced items, and wherein:  
         [0017]      FIG. 1  is a schematic illustration of an exemplary operating environment in which a computing resource allocation system of the present invention can be used;  
         [0018]      FIG. 2  is a block diagram of the high-level architecture of the computing resource allocation system of  FIG. 1 ; and  
         [0019]      FIG. 3  is comprised of  FIGS. 3A and 3B  and represents a process flow chart illustrating a method of operation of the computing resource allocation system of  FIGS. 1 and 2 .  
     
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS  
       [0020]     The following definitions and explanations provide background information pertaining to the technical field of the present invention, and are intended to facilitate the understanding of the present invention without limiting its scope:  
         [0021]     Internet: A collection of interconnected public and private computer networks that are linked together with routers by a set of standards protocols to form a global, distributed network.  
         [0022]     Software Metric (Metric): Software measurements that use numerical ratings to quantify allowable performance of an application or a computer system.  
         [0023]     World Wide Web (WWW, also Web): An Internet client-server hypertext distributed information retrieval system.  
         [0024]      FIG. 1  portrays an exemplary overall environment in which a system, a service, and an associated method for efficient computing resource allocation according to the present invention may be used. System  10  comprises a software programming code or a computer program product that is typically embedded within, or installed on a host server  15 . Alternatively, system  10  can be saved on a suitable storage medium such as a diskette, a CD, a hard drive, or like devices.  
         [0025]     Users, such as remote Internet users, are represented by a variety of computers such as computers  20 ,  25 ,  30 , and can access the host server  15  through a network  35 . Computers  20 ,  25 ,  30  comprise desktop computers, workstations, laptop computers, mainframe computers, or any other device that may be connected to a network and that operates software applications or performs computations. Computers  20 ,  25 ,  30  each comprise software that allows the user to interface securely with the host server  15 . The host server  15  is connected to network  40  via a communications link  40  such as a telephone, cable, or satellite link.  
         [0026]     Computers  20 ,  25 ,  30 , can be connected to network  40  via communications links  45 ,  50 ,  55 , respectively. While system  10  is described in terms of network  35 , computers  20 ,  25 ,  30  may also access system  10  locally rather than remotely. Computers  20 ,  25 ,  30  may access system  10  either manually, or automatically through the use of an application.  
         [0027]      FIG. 2  is a block diagram illustrating a high-level architecture of system  10 . System  10  comprises a performance agent  205 , a resource allocator  210 , a resource identification algorithm  215 , and a collection database  220  (also referenced herein as collection dB  220 ). The performance agent  205  is installed on the computing resource of the user such as computers  20 ,  25 ,  30 . The resource allocator  210 , the resource identification algorithm  215 , and the collection dB  220  are installed on the server  15 .  
         [0028]     The performance agent  205  captures performance data from computers  20 ,  25 ,  30  comprising: 
        time percentages in which computers  20 ,  25 ,  30  are engaged in “excessive” paging activity, where “excessive” may be defined according to variable thresholds;     time percentages in which computers  20 ,  25 ,  30  are engaged in “excessive” CPU utilization, where “excessive” may be defined according to variable thresholds;     time percentages in which computers  20 ,  25 ,  30  are constrained by I/O devices;     associations of system or application processes with the time percentages mentioned above; and     events or errors that are logged by system  10  such as, for example, a failed installation of a software application due to less than minimum system requirements of the software application available from the computers  20 ,  25 ,  30 .        
 
         [0034]     Furthermore, the performance agent  205  may capture any performance parameter on computers  20 ,  25 ,  30  that can be used to determine how well computers  20 ,  25 ,  30  are performing and the extent to which computers  20 ,  25 ,  30  are being used by the user. In an embodiment, the performance parameters are based on average utilization of computers  20 ,  25 ,  30  rather than time percentages.  
         [0035]     The performance agent  205  collects performance data regarding the performance of computers  20 ,  25 ,  30  and software applications running on computers  20 ,  25 ,  30  to obtain further insight into the source of the “excessive” resource activity. An “excessive” resource activity does not necessarily indicate a need for a component upgrade, a component replacement, or a new computing resource. For example, a process that takes more than 90% CPU utilization may indicate a user-invoked activity. A different process that takes more than 90% CPU utilization may indicate a sub-optimal software configuration problem related to, for example, file system indexing. In the former case, the excessive CPU utilization is considered more heavily by system  10  than the latter. System  10  recommends different responses to these two exemplary scenarios that optimally correct the source of the “excessive” resource activity.  
         [0036]     Performance data collected by the performance agent  205  are forwarded on a periodic basis to the resource allocator  210  on server  15 . The resource identification algorithm  215  then applies policy rules to the collected data.  
         [0037]     The determination by system  10  of acceptable excessive paging in computers  20 ,  25 ,  30  can be determined by employee band and employee function. For example, a much higher level of excessive computer paging is tolerated for clerical employees at band  6  than for developer employees at band  10 .  
         [0038]     The acceptable limits that are initially defined can be altered dynamically from an end-user standpoint by related events. A person, such as the help center technician, could update the metric table, at which point all monitored stations will be evaluated based on the new value(s). For example, if a specific class of users constantly calls to complain about performance when paging is above 15%, and the current metric table allows for 20%, a decision may be made to reduce the current value to 14%. This process may also be automated. As an example, system  10  could periodically query the help center database, and if the trend just described is detected, the metric table is automatically updated.  
         [0039]     As an example, the data collected by a “help center” operated by an information technology division for the employees within a corporation is a good indication of what employees consider acceptable performance ranges. If several employees of a like category call the “help center” and complain of performance problems with similar characteristics then the thresholds can be automatically weighted up or down accordingly by system  10 .  
         [0040]     When the resource allocator  210  identifies a trend of unacceptable excessive paging on, for example, computer  20 , the resource allocator  210  outputs an request for an automatic distribution of memory for computer  20 . The unacceptable excessive paging is defined by the guidelines laid out for the employee level and function. System  10  may use other matrixes to define levels of unacceptable performance by computers  20 ,  25 ,  30  to identify other components that may require upgrades to bring performance of computers  20 ,  25 ,  30  into acceptable bounds. System  10  can use matrixes to define levels of unacceptable performance leading to upgrades of components such as, for example, CPUs, network adapters, disk adapters, disk drives, and other peripheral adapters and devices that are well known in computing.  
         [0041]     Comparison of fundamental measurements by system  10 , such as CPU over-utilization, may indicate the need for a complete replacement of, for example, computer  20 . A table comprises acceptable performance limits for collected parameters that is used to determine whether computers  20 ,  25 ,  30  require replacement. Partial upgrades of computers  20 ,  25 ,  30  correct performance issues with paging or other I/O considerations. However, processor-based constraints may be indicative of a need for a new computing resource, particularly given the fact that having a service provider add or replace a CPU within a computing resource can cost as much as a new computing resource. In addition, the replaced computing resource can then be redeployed to an employee with lesser computing needs.  
         [0042]     System  10  may determine the need for a replacement of computers  20 ,  25 ,  30  by examining a composite of various performance parameters. Any one performance parameter that exceeds acceptable limits may indicate only the need for a component upgrade. However, system  10  may determine that several performance parameters exceed acceptable limits concurrently, indicating a need for a new computing resource. For example, a computing resource that demonstrates excessive paging, I/O, and marginally excessive CPU over-utilization may be a candidate for replacement based upon the logistical and cost concerns of upgrading multiple individual components on the computing resource.  
         [0043]     System  10  further sets acceptable thresholds for error conditions according to band and job function that indicate a need for component upgrade or replacement of computers  20 ,  25 ,  30 . For many error conditions, the error threshold may be at or very close to zero. However, non-critical errors may be tolerated in certain situations such as, for example, temporary errors or retry conditions. The non-critical errors are particularly dependent on functions that are being performed by an employee.  
         [0044]     The performance agent  205  collects error information on computers  20 ,  25 ,  30  by a “trap” method. The trapped error information is forwarded to the resource allocator  210 . Resource allocator  210  compares the error information to reference information on related websites operated by vendors of computers  20 ,  25 ,  30  to assist in determining problems with computers  20 ,  25 ,  30 . For example, error information collected when a computing resource using a Windows® operating system “blue-screens” can be referenced in the MS Knowledge base. Several known traps of Windows® operating systems are strong indications of memory errors. Consequently, this information is used by system  10  in determining the need for automatic evaluation of the errors and possible recommendation for replacement of the computing resource.  
         [0045]      FIG. 3  ( FIGS. 3A and 3B ) is a flow chart illustrating a method  300  of operation of system  10 . At step  305 , the performance agent  205  is installed on a computing resource of a user such as computer  20 . Each computing resource has an ID that associates the computing resource with the user. The performance agent  205  collects performance data from the computing resource on a scheduled basis at step  310 . Performance data comprises errors experienced during operation of the computing resource and measurements quantifying performance of the computing resource. The performance agent  205  forwards performance data and the ID of the computing resource to the resource allocator  210  in step  315 . The resource allocator  210  identifies the user of the computing resource at step  320 , providing the employee level and job category of the user for use in analyzing the collected performance data.  
         [0046]     The resource allocator  210  sorts the performance data with respect to the employee level and job category of the user (step  325 ). The resource identification algorithm  215  applies policy rules to the performance data at step  330 , comparing the performance data with a set of metrics that describe allowable performance for a computing resource for a user at a specific employee level and job category. At step  335 , the resource identification algorithm  215  archives performance data that falls below all metrics n the collection dB  220 . In an embodiment, performance data that falls below all metrics is discarded. Performance data stored in the collection dB  220  may be accessed by system  10  to determine a cumulative performance of a computing resource, aiding in a determination of component upgrade, component replacement, computing resource upgrade, or computing resource replacement.  
         [0047]     The resource identification algorithm  215  analyzes errors and instances in which the collection data exceeds one or more performance metrics (step  340 ). At decision step  345 , the resource identification algorithm  215  determines whether any system errors in the computing resource have occurred. If yes, the resource identification algorithm  215  proceeds to step  350  and recommends replacing or upgrading the computing resource. If the specifications of the present computing resource are adequate for the employee level and job category of the user, the resource identification algorithm  215  recommends a replacement of the computing resource. Otherwise, the resource identification algorithm recommends a computing resource upgrade.  
         [0048]     If at decision step  345  the resource identification algorithm  215  detects no system errors, processing proceeds to decision step  355 . If at decision step  355  the number of errors or instances in which performance data exceeds metrics is equal to one, the resource identification algorithm  215  determines the source of the performance metric failure at step  360 . The resource identification algorithm  215  recommends a replacement or upgrade for the component that is the source of the error or the instance by which performance data exceeds metrics (step  365 ). If the specifications of the component are adequate for the employee level and job category of the user, the resource identification algorithm  215  recommends a component replacement. Otherwise, the resource identification algorithm  215  recommends a component upgrade.  
         [0049]     If at decision step  355  the sum of errors and instances in which performance data exceeds metrics is greater than one, the resource identification algorithm  215  proceeds to decision step  370 . The resource identification algorithm  215  determines whether system performance of the computing resource exceeds a metrics threshold. If yes, the resource identification algorithm  215  recommends a replacement or upgrade of the computing resource at step  350 . Otherwise, the resource identification algorithm  215  determines the sources of the metric failures at step  375 .  
         [0050]     There may be several components in the computing resource that are causing metrics failure. If the specifications of the components are adequate for the employee level and job category of the user, the resource identification algorithm  215  recommends replacement of the components. Otherwise, the resource identification algorithm  215  recommends upgrade of the components. The resource identification algorithm  215  may recommend replacement of some components and upgrade of other components, depending on the analysis performed at step  375 .  
         [0051]     The resource identification algorithm  215  may recommend a replacement of a component or the computing resource based upon a single performance data collection point. For example, if performance data is collected once a night, recommendation may be initiated based upon performance data collected in one night. Alternatively, the replacement recommendation may be dependent upon composite parameters established over multiple performance data collection points. To determine composite parameters, the resource identification algorithm  215  may use a moving average. The moving average may comprise performance data from a computing resource over a long term during which a replacement becomes necessary. Conversely, the moving average may comprise performance data from a computing resource in which a consecutive number of data points in the performance data occur above the thresholds of the metrics.  
         [0052]     It is to be understood that the specific embodiments of the invention that have been described are merely illustrative of certain applications of the principle of the present invention. Numerous modifications may be made to a system, method, and service for efficient allocation of computing resources among users described herein without departing from the spirit and scope of the present invention. Moreover, while the present invention is described for illustration purpose only in relation to the WWW, it should be clear that the invention is applicable as well to, for example, any network in which computers or computing resources may be connected together for communications purposes between the computers.