Abstract:
An autonomic computing system for dynamically managing a performance model for a data center. Measured performance data for a data processing system and performance model performance data from a performance model of the data processing system are obtained from which is generated estimated performance data. Upon comparison, if a difference between the measured performance data and the estimated performance data falls within defined limits, the performance module in the performance model structure is identified as an accurate model. If the difference does not fall within defined limits, the estimated performance data, the model performance data, and the measured performance data is analyzed to estimate new performance parameters for the performance model structure. Responsive to estimating new performance parameters for the performance model structure, the performance model structure is updated with the estimated new performance parameters.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    1. Field of the Invention 
         [0002]    The present invention relates generally to an improved data processing system, and in particular to a computer implemented method, system, and computer program product for dynamically managing a performance model for a data center. 
         [0003]    2. Description of the Related Art 
         [0004]    An autonomic computing system is a system that exhibits self-management, freeing administrators of low-level task management while delivering an optimized system. In an autonomic computing system, an autonomic manager controls the parameters of a managed element, makes decisions about runtime changes in the managed element and its surrounding environment, and performs those changes. An administrator does not control the system directly; rather, the administrator defines general policies and rules that serve as an input for the self-management process. For instance, the goals of the autonomic manager may be to maintain performance parameters of the system within limits specified by service level objectives (SLOs), and it may accomplish the goals by (a) measuring performance parameters of the system, such as response time, utilization, throughput, and workload, (b) comparing the measured parameters time with those specified SLO, and, (c) if the measured performance parameters are not within the limits, making changes in the system to bring the parameters within SLO limits. 
         [0005]    The type of changes made to the system may include the tuning of runtime performance parameters (e.g., threading level, cache size, number of sessions, and the like), workload balancing across many processing elements, and provisioning the system with more processing elements that share the workload. However, there are a number of problems which may arise when creating an autonomic manager that implements the above policies and controls the performance of the system. One problem may be the lack of quantitative assessment tools, which are needed to determine what performance parameters to change in the system to bring the parameters within SLO limits and by how much. 
         [0006]    One example of a known autonomic computing system implementation is Tivoli Intelligent Orchestrator (TIO), in which utilization of a processing element is monitored by TIO, and if the utilization goes above a threshold, another processing unit is added to the system to adhere to SLO limits. However, there are two main drawbacks of the TIO method. First, while the TIO method monitors utilization, what matters for users is the response time of the system rather than the utilization. Even if there is a correspondence between the utilization and response time that was established before setting the utilization threshold, that correspondence may not hold at runtime due to many perturbations that can affect the system. Second, by monitoring the utilization, there may not be a way to dynamically determine how many processing elements to add to maintain the response time within the limits. By using a predetermined change increment (e.g., always add “1” to the processing unit), it may take several steps to provide the necessary capacity, which may introduce unacceptable delays in bringing the response time within SLO limits. 
         [0007]    Known autonomic computing systems also may comprise performance modeling tools such as analytical queuing models that mimic the system from a performance point of view. 
       SUMMARY OF THE INVENTION 
       [0008]    The illustrative embodiments provide an autonomic computing system for dynamically managing a performance model for a data center. The autonomic computing system obtains measured performance data for a data processing system and model performance data from a model of the data processing system. Estimated performance data is generated based on the measured performance data and the model performance data. Upon comparison, if a difference between the measured performance data and the estimated performance data falls within defined limits, the performance module in the performance model structure is identified as an accurate model. If the difference does not fall within defined limits, the estimated performance data, the model performance data, and the measured performance data is analyzed to estimate new performance parameters for the performance model structure. Responsive to estimating new performance parameters for the performance model structure, the performance model structure is updated with the estimated new performance parameters. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0009]    The novel features believed characteristic of the illustrative embodiments are set forth in the appended claims. The illustrative embodiments, themselves, however, as well as a preferred mode of use, further objectives, and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings, wherein: 
           [0010]      FIG. 1  is a pictorial representation of a data processing system in which the illustrative embodiments may be implemented; 
           [0011]      FIG. 2  is a block diagram of a data processing system in which the illustrative embodiments may be implemented; 
           [0012]      FIG. 3  is a block diagram of an exemplary autonomic manager with which the illustrative embodiments may be implemented; 
           [0013]      FIG. 4  is a diagram illustrating the exemplary operations of a model estimator in accordance with the illustrative embodiments; 
           [0014]      FIG. 5  is a diagram illustrating exemplary components with which the illustrative embodiments may be implemented and their interactions; 
           [0015]      FIG. 6A  is an XML representation of data describing an exemplary model and load in accordance with the illustrative embodiments; 
           [0016]      FIG. 6B  is an XML representation of data describing exemplary data estimated by the solver in accordance with the illustrative embodiments; 
           [0017]      FIG. 6C  is an XML representation of data describing exemplary data measured by the data acquisition engine in accordance with the illustrative embodiments; 
           [0018]      FIG. 6D  is an XML representation of data describing exemplary estimated parameters and arrival rates for a model in accordance with the illustrative embodiments; 
           [0019]      FIG. 7  is a graph illustrating measured response time and estimated response time for several exemplary workloads over a period of time in accordance with the illustrating embodiments; and 
           [0020]      FIG. 8  is a flowchart of a process for dynamically managing a performance model for a data center in accordance with the illustrative embodiments. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0021]    With reference now to the figures and in particular with reference to  FIGS. 1-2 , exemplary diagrams of data processing environments are provided in which illustrative embodiments may be implemented. It should be appreciated that  FIGS. 1-2  are only exemplary and are not intended to assert or imply any limitation with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made. 
         [0022]    With reference now to the figures,  FIG. 1  depicts a pictorial representation of a network of data processing systems in which illustrative embodiments may be implemented. Network data processing system  100  is a network of computers in which embodiments may be implemented. Network data processing system  100  contains network  102 , which is the medium used to provide communications links between various devices and computers connected together within network data processing system  100 . Network  102  may include connections, such as wire, wireless communication links, or fiber optic cables. 
         [0023]    In the depicted example, server  104  and server  106  connect to network  102  along with storage unit  108 . In addition, clients  110 ,  112 , and  114  connect to network  102 . These clients  110 ,  112 , and  114  may be, for example, personal computers or network computers. In the depicted example, server  104  provides data, such as boot files, operating system images, and applications to clients  110 ,  112 , and  114 . Clients  110 ,  112 , and  114  are clients to server  104  in this example. Network data processing system  100  may include additional servers, clients, and other devices not shown. 
         [0024]    In the depicted example, network data processing system  100  is the Internet with network  102  representing a worldwide collection of networks and gateways that use the Transmission Control Protocol/Internet Protocol (TCP/IP) suite of protocols to communicate with one another. At the heart of the Internet is a backbone of high-speed data communication lines between major nodes or host computers, consisting of thousands of commercial, governmental, educational and other computer systems that route data and messages. Of course, network data processing system  100  also may be implemented as a number of different types of networks, such as for example, an intranet, a local area network (LAN), or a wide area network (WAN).  FIG. 1  is intended as an example, and not as an architectural limitation for different embodiments. 
         [0025]    With reference now to  FIG. 2 , a block diagram of a data processing system is shown in which illustrative embodiments may be implemented. Data processing system  200  is an example of a computer, such as server  104  or client  110  in  FIG. 1 , in which computer usable code or instructions implementing the processes may be located for the illustrative embodiments. 
         [0026]    In the depicted example, data processing system  200  employs a hub architecture including a north bridge and memory controller hub (MCH)  202  and a south bridge and input/output (I/O) controller hub (ICH)  204 . Processing unit  206 , main memory  208 , and graphics processor  210  are coupled to north bridge and memory controller hub  202 . Processing unit  206  may contain one or more processors and even may be implemented using one or more heterogeneous processor systems. Graphics processor  210  may be coupled to the MCH through an accelerated graphics port (AGP), for example. 
         [0027]    In the depicted example, local area network (LAN) adapter  212  is coupled to south bridge and I/O controller hub  204  and audio adapter  216 , keyboard and mouse adapter  220 , modem  222 , read only memory (ROM)  224 , universal serial bus (USB) ports and other communications ports  232 , and PCI/PCIe devices  234  are coupled to south bridge and I/O controller hub  204  through bus  238 , and hard disk drive (HDD)  226  and CD-ROM drive  230  are coupled to south bridge and I/O controller hub  204  through bus  240 . PCI/PCIe devices may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. ROM  224  may be, for example, a flash binary input/output system (BIOS). Hard disk drive  226  and CD-ROM drive  230  may use, for example, an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. A super I/O (SIO) device  236  may be coupled to south bridge and I/O controller hub  204 . 
         [0028]    An operating system runs on processing unit  206  and coordinates and provides control of various components within data processing system  200  in  FIG. 2 . The operating system may be a commercially available operating system such as Microsoft® Windows® XP (Microsoft and Windows are trademarks of Microsoft Corporation in the United States, other countries, or both). An object oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provides calls to the operating system from Java programs or applications executing on data processing system  200 . Java and all Java-based trademarks are trademarks of Sun Microsystems, Inc. in the United States, other countries, or both. 
         [0029]    Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as hard disk drive  226 , and may be loaded into main memory  208  for execution by processing unit  206 . The processes of the illustrative embodiments may be performed by processing unit  206  using computer implemented instructions, which may be located in a memory such as, for example, main memory  208 , read only memory  224 , or in one or more peripheral devices. 
         [0030]    The hardware in  FIGS. 1-2  may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in  FIGS. 1-2 . Also, the processes of the illustrative embodiments may be applied to a multiprocessor data processing system. 
         [0031]    In some illustrative examples, data processing system  200  may be a personal digital assistant (PDA), which is generally configured with flash memory to provide non-volatile memory for storing operating system files and/or user-generated data. A bus system may be comprised of one or more buses, such as a system bus, an I/O bus and a PCI bus. Of course the bus system may be implemented using any type of communications fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communications unit may include one or more devices used to transmit and receive data, such as a modem or a network adapter. A memory may be, for example, main memory  208  or a cache such as found in north bridge and memory controller hub  202 . A processing unit may include one or more processors or CPUs. The depicted examples in  FIGS. 1-2  and above-described examples are not meant to imply architectural limitations. For example, data processing system  200  also may be a tablet computer, laptop computer, or telephone device in addition to taking the form of a PDA. 
         [0032]    The illustrative embodiments provide a computer implemented method, system, and computer program product or dynamically managing a performance model for a data center. In particular, the illustrative embodiments provide a performance model, which comprises a mathematical construct that mimics a system from a performance point of view, and a model estimator, which provides a programmatic method of building the performance model. The performance model and the model estimator are used concurrently to identify and implement quantitative changes in the system. In this manner, the use of the performance model in conjunction with the model estimator allows for programmatically determining performance parameters to change in a system, as well as estimating the results and magnitude of implementing those parameter changes. Examples of performance parameter changes may include, but are not limited to, changing the number of threads in use to accommodate the number of users, adding another server to the cluster to balance system loads, adding another instance of an application, and the like. 
         [0033]    In one particular implementation, the illustrative embodiments may be implemented as part of Tivoli Intelligent Orchestrator (TIO), which is a product available from International Business Machines Corporation. Tivoli and various Tivoli derivatives are trademarks of International Business Machines Corporation in the United States, other countries, or both. The illustrative embodiments may also be implemented in any system which shares computing resources, such as a networked computing system or a cluster, or a computing system with multiple processors. 
         [0034]      FIG. 3  is a block diagram of an exemplary autonomic manager with which the illustrative embodiments may be implemented. Autonomic manager  300  may be implemented in a data processing system, such as data processing system  200  in  FIG. 2 . In this illustrative example, autonomic manager  300  comprises a feedback-based internal structure of monitoring component  302 , model estimator  304 , predictive performance model  306 , and decision maker  308 . 
         [0035]    Monitoring component  302  measures the performance of a system, such as, for example, network data processing system  100  in  FIG. 1 . Monitoring component  302  comprises a data store that collects the performance data of the system over a time period. Examples of monitoring component  302  include Tivoli Monitoring for Transaction Performance (TMTP) and Performance Monitoring Interface (PMI), which are products available from International Business Machines Corporation. The performance data measured by monitoring component  302  may include performance data such as resource response time, utilization, or throughput (e.g., arrival rate or number of users). Monitoring component  302  periodically samples the performance data and provides the data to model estimator  304 . 
         [0036]    Model estimator  304  builds predictive performance model  306 , and predictive performance model  306  is used as a feedback for model estimator  304 . Model estimator  304  estimates certain performance parameters of the system based on the measured performance data obtained from monitoring component  302  and the model performance data output from predictive performance model  306 . Model estimator  304  may perform an estimation using any known optimal recursive data processing algorithm, such as a Kalman filter. A Kalman filter combines all available measurement data (system parameters measured by monitoring component  302 ), plus prior knowledge about the system and measuring devices (model parameters output by predictive performance model  306 ), to produce an estimate of the desired parameters in such a manner that the estimation error covariance is minimized statistically. In situations where some parameters of the system are not measured by monitoring component  302  to reduce overhead, model estimator  304  uses a predictor which estimates the values of these non-measured parameters of the system. Model estimator  304  also uses a corrector component which, based on a comparison of the measured real system performance data and the model performance data, corrects the values of the estimated parameters in predictive performance model  306 . 
         [0037]    Decision maker  308  compares Service Level Objects (SLO) with the measured system and model performance data. If decision maker  308  determines that, based on the comparison, there is need to improve the performance of the system, decision maker  308  queries predictive performance model  306  to identify which processing element in the system needs to be tuned or provisioned with more hardware and how much provisioning or tuning should be done. Decision maker  308  then issues provisioning or tuning commands to the execution elements. The commands issued may be influenced by the local policies of decision maker  308 . These policies may include references to upper limits on the number of the resources to be provisioned at any given time, the time intervals between two consecutive provisioning commands, etc. 
         [0038]      FIG. 4  is a diagram illustrating the exemplary operations of a model estimator, such as model estimator  304  in  FIG. 3 , in accordance with the illustrative embodiments. For a particular workload (u)  402 , model estimator  404  monitors measured performance output (z)  406  of system  408  and the measured performance output (y)  410  of predictive performance model  412 . Predictive performance model  412  is an example of predictive performance model  306  in  FIG. 3 . As previously mentioned, the outputs are easily accessible measured values, such as resource response time (R), utilization (U), or throughput (X). Model estimator  404  compares measured performance output (z)  406  against the estimated performance output (y)  410 . The difference between performance output (z)  406  and performance output (y)  410 , is defined as estimation error (e)  414  and is used by model estimator  404  to estimate new parameters (x)  416  for the predictive performance model  412 . The estimation of parameters (x)  416  is made with the goal of minimizing estimation error (e)  414 , thereby allowing the model parameters to mimic the performance of the system as closely as possible. Estimated parameters (x)  416  may include parameters that are hard to measure in the system, like service times or the number of invocations between the components of the system, and which have a direct influence on the performance outputs. As shown, predictive performance model  412  has a predefined dependency between estimated parameters (x)  416  and measured performance output (y)  410 . This dependency fits with the historical data as well as with the current measures. With estimated parameters (x)  416  and with workload (u)  402 , model estimator  404  estimates new performance output (y)  410 . New performance output (y)  410  and performance output (z)  406  are then compared and the process may be repeated in as many iterations as needed in order for the estimated performance data to converge with the measured performance data of the system. 
         [0039]      FIG. 5  is a diagram illustrating exemplary components in an autonomic computing system with which the illustrative embodiments may be implemented and their interactions. In  FIG. 5 , system  514  denotes a computing system such as network data processing system  100  in  FIG. 1  or denoted system  408  in  FIG. 4 . All the other components in  FIG. 5  are further details of monitoring component  302 , model estimator  304 , predictive performance model  306 , and decision maker  308  in  FIG. 3 . In exemplary autonomic computing system  500 , parallelograms denote data structures, and rectangles denote active elements, such as function. For instance, data acquisition engine  502  is a function within monitoring component  302  in  FIG. 3 , coordinator  504  and parameter estimator  506  are functions within model estimator  304  in  FIG. 3 , solver  508  and model structure  510  are subcomponents of the predictive performance model  306  in  FIG. 3 . Continuous lines are used to denote the data flow, and dashed lines denote the control flow. The goal of the component interactions is to make the performance data in estimations data structure  512  match the performance data measured from system  514 . 
         [0040]    Data acquisition engine  502  first collects performance data from system  514 . If the collected performance data comprises workload data, data acquisition engine  502  updates load structure  516  with the workload data. Workload data may include performance data such as the number of users, user think times, arrival rate, or other workload data entities that one skilled in the art of modeling will recognize. 
         [0041]    Coordinator  504  obtains the measured performance data from data acquisition engine  502  and invokes solver  508  to check if the performance data collected by data acquisition engine  502  matches the performance values estimated by solver  508 . Solver  508  then receives an input of measured workload data from load structure  516  and an input of model data from model structure  510 . When the inputs are received, solver  508  may combine the workload data against the performance model data using any well known performance modeling algorithm. 
         [0042]    Based on the combination of the load and model inputs, solver  508  generates estimations data structure  512 , which comprises estimated parameters for the system. Coordinator  504  then compares the measured performance data collected by data acquisition engine  502  with the estimated data from estimations data structure  512 . If the difference between the measured performance data and the estimated data is within the limits set by the system administrator (e.g., less than 10% of measured performance data), then the current performance model in model structure  510  is determined to be an accurate model and the workload in load structure  516  is determined to be an accurate workload. Control may then be passed to decision maker  518 , and decision maker  518  is able to now use the performance model in model  510  as a substitute for the real system. 
         [0043]    If the difference between the measured performance data and the estimated data is not within the limits defined by the system administrator, parameter estimator  506  is launched. Parameter estimator  506  analyzes all of the measured data, including the estimated performance data, the model performance data, and the workload performance data. This analysis may include using well known algorithms such as a Kalman filter, for example, to estimate new parameters for model structure  510  and load structure  516 . Parameter estimator  506  then updates model structure  510  with the new estimated parameters. 
         [0044]    In should be noted that several iterations of the process described herein may be needed in order for parameter values estimated by solver  508  to converge or approach the parameter values measured by data acquisition engine  502 . When the convergence occurs, coordinator  504  signals decision maker  518  that a good model of the system has been obtained. At this point, decision maker  518  may now use the model in model  510  as a substitute for the real system. Decision maker  518  may play “what if” scenarios with the model. These scenarios may include additions and removal of hardware and software resources and their effect on the overall system performance. 
         [0045]      FIGS. 6A-6D  are XML representations of exemplary data samples in accordance with the illustrative embodiments. In particular,  FIG. 6A  is an XML representation of data describing an exemplary model and load in accordance with the illustrative embodiments. Data sample  602  may be used as input to the solver comprising measured workload data (e.g., arrivalrate=“0.4”  604 ) from the load structure for a client (e.g., marin09.torolab.ibm.com  606 ), and input of estimated model data (e.g., demand=“30”  608 ) from the model structure for a server (e.g., marin.torolab.ibm.com  610 ). 
         [0046]      FIG. 6B  is an XML representation of data describing exemplary data estimated by the solver in accordance with the illustrative embodiments. Data sample  612  may be used by the solver to indicate estimated parameters of a resource. In this illustrative example, data sample  612  shows the estimated response time  614  of the server “marin.torolab.ibm.com”. 
         [0047]      FIG. 6C  is an XML representation of data describing exemplary data measured by the data acquisition engine in accordance with the illustrative embodiments. Data sample  622  may be used by the solver to indicate measured parameters of a resource. In this illustrative example, data sample  622  shows the measured response time  624  of the server “marin.torolab.ibm.com” which was measured by the data acquisitions engine. 
         [0048]      FIG. 6D  is an XML representation of data describing exemplary estimated demand and arrival rates for a model in accordance with the illustrative embodiments. Data sample  632  may be used by the parameter estimator to indicate estimated parameters of a resource. In this illustrative example, the estimated demand on the “marin.torolab.ibm.com” server  634  and the estimated response time for the client “marin09.torolab.ibm.com”  636  is shown. 
         [0049]      FIG. 7  illustrates measured response time (real system) and the estimated response time (model) for several exemplary workloads over a period of time in graph form. The number of users in this illustrative example is shown by line  702 . The estimated parameters used by the model estimator comprise the service time, which is shown by line  704 . The measured response time of the real system for several workloads is shown by line  706 . The estimated response time generated using the model is shown by line  708 . 
         [0050]      FIG. 8  is a flowchart of a process for dynamically managing a performance model for a data center in accordance with the illustrative embodiments. The process illustrated in  FIG. 8  may be implemented using components in autonomic computing system  500  in  FIG. 5 . The process begins by having a data acquisition engine collect measured performance data from the system (step  802 ). The data acquisition engine then updates a load structure in the autonomic computing system with the measured performance data (step  804 ). 
         [0051]    A coordinator component in the autonomic computing system then obtains the measured performance data from the load structure and invokes a solver component in order to check whether or not the measured performance data and the performance data estimated by the solver are within limits set by the system administrator (step  806 ). To determine if the values are within acceptable limits, the solver receives an input of measured performance data from the load structure and an input of model performance data from a model structure (step  808 ). The solver may combine the measured data and the model data using any well known performance modeling algorithm. 
         [0052]    Based on the measured performance data and model performance data inputs, the solver then generates estimated performance data for the system (step  810 ). The coordinator then compares the measured performance data with the estimated performance data to determine if the measured performance data and the estimated performance data are within limits set by the system administrator (step  812 ). If the values are within acceptable limits (‘yes’ output of step  812 ), the performance model in the model structure is determined to be an accurate model and the workload in the load structure is determined to be an accurate workload. The coordinator signals the decision maker that a good model of the system has been obtained and control is passed to the decision maker (step  814 ). The decision maker is able to now use the model as a substitute for the real system, and the process terminates thereafter. 
         [0053]    Turning back to step  812 , if the measured performance data and the estimated performance data are not within the limits (‘no’ output of step  812 ), a parameter estimator is launched (step  816 ). The parameter estimator analyzes all of the performance data, including the estimated performance data, the model performance data, and the measured performance data, in order to estimate new performance parameters for the model structure and the load structure (step  818 ). The parameter estimator then updates the model structure with the new estimated parameters. The process may loop back to step  802  until the parameter values estimated by the solver (estimated parameter values) converge with the parameter values measured by the data acquisition engine (real system parameter values) as illustrated by the ‘yes’ output of step  812 . 
         [0054]    The invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. 
         [0055]    Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any tangible apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
         [0056]    The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD. 
         [0057]    A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. 
         [0058]    Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. 
         [0059]    Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters. 
         [0060]    The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention, the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.