Patent Abstract:
A method, system and computer-usable medium are disclosed for monitoring a computer system to predict failed or degraded operational states and respond with an alarm or corrective action. Resource collection and consumption are analyzed to derive velocity and acceleration. A hidden Markov model with the resource collection and consumption data as observation spaces predicts computer system state spaced indicative of a failed or degraded computer system operating state.

Full Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates in general to the field of computers and similar technologies, and in particular to software utilized in this field. Still more particularly, it relates to a method, system and computer-usable medium for predictive computer system resource monitoring. 
     Description of the Related Art 
     Computer systems often coordinate a variety of resources to accomplish desired tasks. For example, a computer system typically includes one or more processors that execute instructions stored in random access memory to generate visual information for presentation at a display with a graphics processor. The processors and memory often support multiple applications simultaneously that perform desired tasks, such as word processing, spreadsheet calculations, web browsing, serving web pages, storing data in persistent storage devices, retrieving data, etc. A resource manager associated with the computer system typically manages the assignment of resources to perform tasks in an efficient manner. For example, an operating system might assign processing threads and memory to applications based upon the workload demands of the applications. As another example, a hypervisor assigns physical processing resources between multiple virtual machines based upon workload demands of the virtual machines and the availability of the physical processing resources. 
     Under normal operating conditions, the resource manager periodically performs a resource collection that aligns processing demands for tasks with physical processing resources. A task is provided access to resources collected for the task based upon the demands faced by the task and based upon the available resources. For example, a web server is provided access to a limited portion of processing threads and memory based upon the demands of client requests placed upon the web server and the demands of other tasks that share the physical resources of the web server. The web server is allowed to use the collected resources and the actual resource consumption of the web server is monitored by the resource manager. At the next resource collection, the resources assigned to the web server adapt based upon the resource consumption of the web server and the resource consumption of other tasks that share resources with the web server. 
     One difficulty that arises with periodic resource collection responsive to monitoring of resource consumption is that a failure in task performance can lead to inefficient resource collection and consumption. For instance, an application that hangs, crashes or otherwise suffers performance degradation can impact other tasks before the difficulty is detected and corrected. In some instances, a relatively minor application error can impact the performance of other more important and unrelated tasks in unpredictable and negative ways. 
     SUMMARY OF THE INVENTION 
     A method, system and computer-usable medium are disclosed for managing computer system operations to perform tasks. Resource collection and consumption are tracked at a computer system, such as by the assignment and use of processing and memory resources at applications. A resource manager derives velocity and acceleration for each of the resource collection and resource consumption information over time. The resource manager applies the resource collection, resource collection velocity, resource collection acceleration, resource consumption, resource consumption velocity and resource consumption acceleration as observation spaces in a hidden Markov model to predict computer system state spaces indicative of a pending computer system failure, such as with a Viterbi algorithm. If the probability of a failure exceeds a threshold, the resource manager performs a responsive action, such as issuing an alarm or a corrective action for the task associated with the predicted failure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention may be better understood, and its numerous objects, features and advantages made apparent to those skilled in the art by referencing the accompanying drawings. The use of the same reference number throughout the several figures designates a like or similar element. 
         FIG. 1  depicts an exemplary client computer in which the present invention may be implemented; 
         FIG. 2  is a simplified block diagram of computer system tasks and resources monitored with a resource manager having a hidden Markov model; 
         FIG. 3  is a generalized flowchart of the operation of a resource manager to monitor computer system tasks and resources with a hidden Markov model; 
         FIG. 4  is a chart of collection velocity and consumption velocity tracked by a resource manager of a computer system; 
         FIG. 5  is a chart of consumption velocity, acceleration and collection velocity for processing and memory resources of a computer system; and 
         FIG. 6  is hidden Markov model output of predicted performance degradation based upon abnormal memory consumption velocity and excessive processing resource consumption. 
     
    
    
     DETAILED DESCRIPTION 
     A method, system and computer-usable medium are disclosed for monitoring computer system task performance with a hidden Markov model generated from derivatives of computer system resource collection and consumption. Predictions by the hidden Markov model of predetermined operational state spaces having a predetermined threshold generate predetermined actions, such as an alarm or a corrective action to the task. 
     As will be appreciated by one skilled in the art, the present invention may be embodied as a method, system, or computer program product. Accordingly, embodiments of the invention may be implemented entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in an embodiment combining software and hardware. These various embodiments may all generally be referred to herein as a “circuit,” “module,” or “system.” Furthermore, the present invention may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium. 
     Any suitable computer usable or computer readable medium may be utilized. The computer-usable or computer-readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, or a magnetic storage device. In the context of this document, a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
     Computer program code for carrying out operations of the present invention may be written in an object oriented programming language such as Java, Smalltalk, C++ or the like. However, the computer program code for carrying out operations of the present invention may also be written in conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Embodiments of the invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
       FIG. 1  is a block diagram of an exemplary client computer  102  in which the present invention may be utilized. Client computer  102  includes a processor unit  104  that is coupled to a system bus  106 . A video adapter  108 , which controls a display  110 , is also coupled to system bus  106 . System bus  106  is coupled via a bus bridge  112  to an Input/Output (I/O) bus  114 . An I/O interface  116  is coupled to I/O bus  114 . The I/O interface  116  affords communication with various I/O devices, including a keyboard  118 , a mouse  120 , a Compact Disk-Read Only Memory (CD-ROM) drive  122 , a floppy disk drive  124 , and a flash drive memory  126 . The format of the ports connected to I/O interface  116  may be any known to those skilled in the art of computer architecture, including but not limited to Universal Serial Bus (USB) ports. 
     Client computer  102  is able to communicate with a service provider server  152  via a network  128  using a network interface  130 , which is coupled to system bus  106 . Network  128  may be an external network such as the Internet, or an internal network such as an Ethernet Network or a Virtual Private Network (VPN). Using network  128 , client computer  102  is able to use the present invention to access service provider server  152 . 
     A hard drive interface  132  is also coupled to system bus  106 . Hard drive interface  132  interfaces with a hard drive  134 . In a preferred embodiment, hard drive  134  populates a system memory  136 , which is also coupled to system bus  106 . Data that populates system memory  136  includes the client computer&#39;s  102  operating system (OS)  138  and software programs  144 . 
     OS  138  includes a shell  140  for providing transparent user access to resources such as software programs  144 . Generally, shell  140  is a program that provides an interpreter and an interface between the user and the operating system. More specifically, shell  140  executes commands that are entered into a command line user interface or from a file. Thus, shell  140  (as it is called in UNIX®), also called a command processor in Windows®, is generally the highest level of the operating system software hierarchy and serves as a command interpreter. The shell provides a system prompt, interprets commands entered by keyboard, mouse, or other user input media, and sends the interpreted command(s) to the appropriate lower levels of the operating system (e.g., a kernel  142 ) for processing. While shell  140  generally is a text-based, line-oriented user interface, the present invention can also support other user interface modes, such as graphical, voice, gestural, etc. 
     As depicted, OS  138  also includes kernel  142 , which includes lower levels of functionality for OS  138 , including essential services required by other parts of OS  138  and software programs  144 , including memory management, process and task management, disk management, and mouse and keyboard management. Software programs  144  may include a browser  146  and email client  148 . Browser  146  includes program modules and instructions enabling a World Wide Web (WWW) client (i.e., client computer  102 ) to send and receive network messages to the Internet using HyperText Transfer Protocol (HTTP) messaging, thus enabling communication with service provider server  152 . In various embodiments, software programs  144  may also include a resource manager  150  that monitors processing and memory resources with a hidden Markov model. In these and other embodiments, the resource manager  150  includes code for implementing the processes described hereinbelow. In one embodiment, client computer  102  is able to download the resource manager  150  from a service provider server  152 . 
     The hardware elements depicted in client computer  102  are not intended to be exhaustive, but rather are representative to highlight components used by the present invention. For instance, client computer  102  may include alternate memory storage devices such as magnetic cassettes, Digital Versatile Disks (DVDs), Bernoulli cartridges, and the like. These and other variations are intended to be within the spirit, scope and intent of the present invention. 
       FIG. 2  is a simplified block diagram of computer system tasks and resources monitored with a resource manager having a hidden Markov model. Resources  200  depicted by the example embodiment of  FIG. 2  include plural processors  202  and plural memory sets  240 . In alternative embodiments, resources may be collected and consumed in a variety of different manners, such as processor threads or cycles and memory blocks or similar quantifications. Resources may also include a variety of different computer system assets, such as networking bandwidth, wireless networking bandwidth, graphics processing graphics memory, persistent storage space, persistent storage accesses, etc. . . . Resources  200  support computer system operations by executing, storing, communicating or otherwise managing information under the direction of applications  206 . In the example embodiment, applications include a server virtual machine  208  that serves web pages and a client virtual machine that retrieves web pages. In alternative embodiments, applications  206  might include a variety of other types of applications that request and consume computer system resources. For example, applications  206  may be cloud-based applications that execute different computer systems, local-based applications that execute on different processors of the same computer system or local-based applications that execute on the same processor with assignable numbers of threads. 
     Resource manager  150  assigns resources  200  to support applications  206  based upon predetermined priority factors. As an example, resource manager  150  is a hypervisor that manages assignment of virtual machines to physical resources. Alternatively, resource manager  150  is a firmware-based management tool that allocates resources  200  to applications  206  based upon the importance of each application  206  and the availability of resources  200 . In one embodiment, resource manager  150  dynamically adjusts resource allocation with a periodic resource collection and resource consumption cycle. At an initial time, resources are collected by each application  206  according to that application&#39;s priority and workload, with resource manager  150  assigning resources in response to a resource collection request of each application based upon the availability of resources. Resource manager  150  determines the availability of resources based in part upon how resources are assigned in response to resource collection request and also based in part upon the actual consumption of resources as monitored by a resource monitor  212 . For instance, resource collection provides a maximum amount of resources that applications  206  may consume but does not determine how much of the resources available to applications  206  are actually used by the applications. Resource monitor  212  may monitor total resource consumption, such as the total number of threads used by a processor or the total amount of memory used to store information, or resource monitor  212  my monitor resources on a per-application or per-task basis. In alternative embodiments, alternative measures of computer system activity may be used. 
     Resource manager  150  monitors computer system operational status in part by predicting potential difficulties before the difficulties cause disruption to computer system operations. Resource manager  150  maintains a resource table  214  that tracks resource collection and consumption by tasks over time, such as by tracking the processor and memory allocated to and used by applications at each collection period. Resource manager  150  also derives velocity and acceleration for each resource collection and resource consumption periodic datapoint. For instance, resource collection velocity is derived by subtracting the immediately previous time period resource collection value from the current resource collection value. Resource collection acceleration is derived by subtracting the immediately previous time period resource collection velocity from the current resource collection velocity. Similarly, resource consumption velocity is derived by subtracting the immediately previous time period resource consumption value from the current resource consumption value. Resource consumption acceleration is derived by subtracting the immediately previous time period resource consumption velocity from the current resource consumption velocity. Although the example embodiment uses periodic resource collection to initiate storage of datapoints, alternative triggers may be used that initiate storage of datapoints at irregular times with the velocity and acceleration data normalized for the time between capture of datapoints. 
     The collection, consumption and derived values stored in resource table  150  are made available to a hidden Markov model engine  216  executing in conjunction with resource manager  150 . Hidden Markov model engine  216  uses the values stored in resource table  150  as observation spaces in a hidden Markov model to predict computer system state spaces, such as by applying a Viterbi algorithm. The computer system state spaces provided by the hidden Markov model present probabilities that the computer system will transition to other states, at least some of which indicate subpar computer system performance. Resource manager  150  stores threshold values for the computer system state spaces so that, if a threshold is met, an action may automatically take place, such as issuing an alarm or performing a preemptive corrective action for the task associated with the threshold, such as rebooting a virtual machine having a likelihood of entering a hung state. In order to improve predictive accuracy of the hidden Markov model, supervised learning may be applied with historic data or, alternatively, unsupervised learning may be applied with a Viterbi algorithm to adjust parameters with real time data. Some examples of computer system state spaces predicted by the hidden Markov model include a normal state, a resource contention state, a hang state, a performance degradation state and a crash state. 
       FIG. 3  is a generalized flowchart of the operation of a resource manager to monitor computer system tasks and resources with a hidden Markov model. The process starts at step  218  with storage a periodic time interval of the task resource collection for each of plural tasks executing under the management of a resource manager. The resource collection represents the resources allocated to each task, such as the processing threads or physical memory allocated to each of plural applications executing on a computer system. At step  220 , velocity and acceleration are derived for the periodic time interval based upon the change in resource collection and resource collection velocity in a previous time interval. At step  224 , task resource consumption data is stored for the time interval for each of the tasks, such as the actual processor and memory resources consumed by each application. At step  226 , velocity and acceleration are derived for the periodic time interval based upon the change in resource consumption and resource consumption velocity in a previous time interval. At step  228 , the resource collection and consumption information, including velocity and acceleration information, is applied in a hidden Markov model as observation spaces to predict computer system state spaces. In alternative embodiments, only portions of the resource table  214  data may be used as observation spaces in the hidden Markov model, such as just collection or just consumption information and/or derivatives. At step  230  a determination is made of whether a response threshold has been met. If not, the process returns to step  218  for monitoring the next time interval. If so, the process continues to step  232  to perform an action associated with the threshold and then returns to step  218 . 
       FIG. 4  is a a chart of collection velocity and consumption velocity tracked by a resource manager of a computer system. As illustrated by the chart, consumption velocity and collection velocity have a relationship that depends upon the logic that sets the amount of resources available to each task. Over time in a steady state operation the collection and consumption velocities tend to follow similar paths, however, variations can be predictive of changes related to computer system operations. 
       FIG. 5  is a chart of consumption velocity, acceleration and collection velocity for processing and memory resources of a computer system. The initial arrow depicted by  FIG. 5  matches a pattern associated with normal computer system operations. The second arrow depicts a memory contention event in which memory use rapidly increases to restrict available memory resources. Even before memory contention peaks at the second arrow in time, the increased probability of a memory contention occurring becomes evident with a hidden Markov model. Acceleration and velocity for CPU and memory collection and consumption indicate the increased risk that memory will have excessive demands. Earlier warning of the upcoming memory contention allows increased time to react and a greater potential for corrective actions to take effect before a predicted difficulty impacts computer system performance. 
       FIG. 6  is hidden Markov model output of predicted performance degradation based upon abnormal memory consumption velocity and excessive processing resource consumption. From an initial start state  234  the observation spaces applied to a hidden Markov model transition matrix and emission matrix provide probabilities to achieve state spaces of normal  244 , hang  246 , performance degradation  248  and crash  250 . The probability of reaching each of the state spaces depends upon the analysis of intermediate system operations reflected by intermediate states of no symptoms  236 , abnormal memory consumption velocity  238 , excessive CPU consumption  240 , and abnormal memory collection velocity  242 . Threshold probability values that trigger a response may depend upon the severity of the state space&#39;s impact on computer system operations. For example, a relatively small probability of entry into a crash state space  250  might trigger a corrective action that intercedes in the performance of the associated task or even initiates a recovery and reboot of the computer system. By comparison, a relatively high probability of a performance degradation state  248  may be needed to take correction action while a low value may provide an alarm to an information technology administrator. 
     Although the present invention has been described in detail, it should be understood that various changes, substitutions and alterations can be made hereto without departing from the spirit and scope of the invention as defined by the appended claims.

Technology Classification (CPC): 6