Metric transport and database load

[0001] None.

[0002] None.

[0003] The present invention relates to the modeling of systems comprising computer software operating on computer hardware. More particularly, the present invention relates to real-time collection of system metrics and the systems and methods for the modeling of system performance parameters as non-linear functions for use in predicting system performance, identifying circumstances at which system performance will become unacceptable, and issuing alarms when system performance is near or beyond unacceptable conditions.

[0004] Computing systems have become an integral part of business, government, and most other aspects of modern life. Most people are likely regrettably familiar with poor performing computer systems. A poor performing computer system may be simply poorly designed and, therefore, fundamentally incapable of performing well. Even well-designed systems will perform poorly, however, if adequate resources to meet the demands placed upon the system are not available. Properly matching the resources available to a system with the demand placed upon the system requires both accurate capacity planning and adequate system testing to predict the resources that will be necessary for the system to function properly at the loads expected for the system.

[0005] Predicting the load that will be placed upon a system may involve a number of issues, and this prediction may be performed in a variety of ways. For example, future load on a system may be predicted using data describing the historical change in the demand for the system. Such data may be collected by monitoring a system or its predecessor, although such historical data may not always be available, particularly for an entirely new system. Other methods, such as incorporating planned marketing efforts or other future events known to be likely to occur, may also be used. The way in which system load is predicted is immaterial to the present invention.

[0006] Regardless how a prediction of future system load is made, a system must have adequate resources to meet that demand if the system is to perform properly. Determining what amount of resources are required to meet a given system demand may also be a complex problem. Those skilled in the art will realize that system testing may be performed, often before a system is deployed, to determine how the system will perform under a variety of loads. System testing may allow system managers to identify the load at which system performance becomes unacceptable, which may coincide with a load at which system performance becomes highly nonlinear. One skilled in the art will also appreciate that such testing can be an enormously complex and expensive proposition, and will further realize that such testing often does not provide accurate information as to at what load a system's performance will deteriorate. One reason for the expense and difficulty of testing is the large number of tests necessary to obtain a reasonably accurate model of system performance.

[0007] One skilled in the art will likely be familiar with the modeling of a system's performance as a linear function of load. One skilled in the art will further realize, however, that a linear model of system performance as a function of load is often a sufficiently accurate depiction of system performance within only a certain range of loads, with the range of loads within which system performance is substantially linear varying for different systems. System performance often becomes non-linear at some point as the load on the system increases. The point at which system performance becomes nonlinear may be referred to as the point at which the linear model breaks down. The load at which a system's performance begins to degrade in a non-linear fashion may be referred to as the knee. At the knee, system throughput increases more slowly while response time increases more quickly. At this point system performance suffers severely, but identifying the knee in testing can be difficult. Accordingly, while a basic linear model theoretically can be obtained with as little as two data points, additional data points are necessary to determine when a linear model of system performance will break down. Obtaining sufficient data points to determine when a linear model of system performance breaks down often requires extensive testing. At the same time, such testing may not yield an accurate model of system performance, particularly as the system moves beyond a load range in which its performance is substantially linear.

[0008] The collection of system metrics in a production environment may be used to monitor system performance. System metrics collected in a production environment may also be used to model system performance. However, linear modeling of system performance using system metrics collected in a production environment will not be likely to yield a better prediction of the system's knee unless the system operates at or beyond that point. Of course, one skilled in the art will appreciate that the purpose of system testing and system modeling is to avoid system operation at and beyond the knee, meaning that if such data is available the modeling and monitoring has already substantially failed.

[0009] A further challenge to using system metrics collected in a production environment is the burden of collecting the metrics. Simply put, collecting system metrics consumes resources. The system to be monitored, and/or associated systems operating with it, must measure, record, and process metrics. Particularly when a system is already facing a shortage of resources, the increased cost of monitoring the system's metrics must occur in an efficient fashion and provide significant benefit to be justified.

[0010] The present invention provides systems and methods to collect metrics from a system operating in a production environment. The collected metrics may be used as a plurality of data points to model system performance by fitting a non-linear curve to the data points. The use of a non-linear curve may better identify the load at which a system's operation will become unacceptable. Systems and methods in accordance with the present invention may also identify correlations between measured system metrics, which may be used to develop further models of system performance. The present invention may also be utilized to identify a point of interest in system performance for use in monitoring a system in a production environment so that an alarm may issue if system performance exceeds predetermined parameters around the point of interest.

[0027] The present invention provides systems and methods for monitoring system performance, identifying correlations between system metrics, modeling system performance, identifying acceptable operating parameters, and issuing alarms if acceptable operating parameters are exceeded, wherein a system comprises computer software operating on computer hardware. The present invention may be used in conjunction with a system comprising any computer software operating on any computer hardware. One example of such a system is an order processing system that receives orders input into a user interface, processes that information, and then provides pertinent information to persons or systems responsible for filling the orders. However, any system comprising software operating on hardware may be monitored and/or modeled using systems and methods in accordance with the present invention. In systems and methods in accordance with the present invention, system metrics may be measured and used to model system performance. For example, data points for system throughput may be obtained at a plurality of loads, and system performance may then be modeled by fitting a non-linear curve to the data points to obtain a non-linear model of system throughput as a function of load. One skilled in the art will appreciate that the data points used in accordance with the present invention to model system performance may be obtained in a variety of ways. By way of example, data points may be obtained through system testing or through monitoring system performance while the system is in use. A system that is in use may be described as being in a production environment. One skilled in the art will appreciate that numerous methods, procedures, techniques, and protocols exist or may be developed for system testing, and that any of these may be used in system testing to obtain data points for use in accordance with the present invention. Likewise, one skilled in the art will appreciate that a variety of methods, procedures, techniques, and protocols exist or may be developed for system monitoring in addition to those described herein, and that any of these may be used to monitor a system operating in its production environment to obtain data points for use in accordance with the system modeling aspects of the present invention.

[0028] In the examples described herein for FIG. 1 through FIG. 6B, the system performance parameters measured and modeled are response time and throughput as a function of load. One skilled in the art will appreciate that other network performance parameters may also be modeled using methods in accordance with the present invention. Response time may be measured as the time required for a system request to be processed, throughput may be measured as the number of system requests processed in a given period of time, and load may be defined as the total number of system users, although one skilled in the art will realize that these parameters may be defined in a number of other ways.

[0029] FIG. 1 illustrates a graph depicting system performance data points at a variety of loads. FIG. 1 illustrates system response time and system throughput as a function of load on a single graph. As can be seen in FIG. 1, twelve data points were collected through testing for both response time and throughput. Solid lines connect collected data points, although no attempt has been made to fit a curve, either linear or nonlinear, to the data points in FIG. 1. In FIG. 1, response time is illustrated on the left vertical axis in units of seconds, throughput is illustrated on the right vertical axis in units of requests processed, and system load is illustrated on the horizontal axis in units of total users. As illustrated in FIG. 1, twelve data points were required to illustrate where system performance became extremely non-linear. As can be seen in FIG. 1, this knee occurs at a load of 1,100 users. It should be realized that FIG. 1 illustrates only a comparatively small set of data points. In actual practice, considerably more data points, and correspondingly more testing, may be required to satisfactorily forecast system performance and to identify the load at which system performance becomes non-linear and the load at which system performance will become unacceptable.

[0030] Referring now to FIG. 2, non-linear curves are illustrated that have been fit to data points for response time and throughput as a function of load. The system modeled in FIG. 2 is the same as the system for which system performance is illustrated in FIG. 1. As can be seen in FIG. 2, only five response time data points and five throughput data points were used to model the non-linear curves. In FIG. 2, response time was modeled by fitting an exponential curve to the response time data points, while the throughput behavior was modeled by fitting a logarithmic curve to the throughput data points. As illustrated in FIG. 2, throughput was modeled as y=180013 Ln(x)+348238 and response time was modeled as y=0.6772e^0.1783x, where x denotes system load. The numerical values in these equations may be determined using curve fitting techniques well known in the art that regressively fit a curve to data points by adjusting the constants of the equation until an optimal fit to the available data points is obtained. As illustrated in FIG. 2, the R² value, which is a measure of the quality of the fit of the curve to the data points, is reasonably high for each curve, being R²=0.888 for response time and R²=0.8757 for throughput. One skilled in the art will appreciate that any method for fitting a curve to collected data points may be used in this process. One skilled in the art will further realize that non-linear curves other than exponential and logarithmic curves may be used if the performance parameter being modeled performs in a manner that lends itself to a different mathematical model. A range in which the distance between the throughput and response time curves is maximized is illustrated by shading in FIG. 2. This range extends from a load of 800 users to a load of 1,050 users. As a load exceeds the optimal range, system performance may be compromised. The optimal range illustrated in FIG. 2 is obtained by identifying the load at which the distance between the response time and throughput curves is greatest. A range of loads may be defined around this identified optimal load in a variety of ways. For example, the optimal range may be defined as the range of loads at which the distance between the curves remains at a predetermined percentage of the maximum distance. Alternatively, the optimal range may be defined as a specific load range around the optimal load, however, such a definition may prove problematic in a rapidly changing system. In comparing FIG. 1 and FIG. 2, it should be noted that FIG. 2 utilizes less than half the data points utilized in FIG. 1. It should be further realized that while the non-linear curves modeled in FIG. 2 do not exactly match the lines connecting data points in FIG. 1, the optimal range identified using the curves in FIG. 2 identifies a maximum load very close to the load at which system response time and system throughput begins to behave in a highly non-linear fashion in FIG. 1. One skilled in the art will further realize that if considerably more data points were obtained in addition to those illustrated in FIG. 1, the resulting graph may likely even more closely resemble the models illustrated in FIG. 2.

[0031] Referring now to FIG. 3 a model of system performance based upon seven data points is illustrated. The system whose performance was modeled in FIG. 2. The curves fit to the data points in FIG. 3 are, for system throughput, y=261828 Ln(x)+297982, which provides an R² value of R²=0.855, and for system response time, y=0.7234e^0.1512x, with an R² value of R²=0.9188. As in FIG. 2, the optimal range determined in FIG. 3 is illustrated using a shaded rectangle. The optimal range determined in the model illustrated in FIG. 3 is a load of 950 users to 1,150 users. It should be noted that the optimal range determined in FIG. 3 using additional data points to fit the curves resembles the optimal range obtained in FIG. 2.

[0032] Referring now to FIG. 4 a model of system performance based upon ten data points is illustrated. The system modeled in FIG. 4 is the same system that was modeled in FIG. 2 and FIG. 3 is illustrated as modeled using ten data points. In modeling system throughput using a curve fit to the data points, the curve fit to the system throughput data points in FIG. 4 is y=389262 Ln(x)+194342, which has a R² value of R²=0.837. In fitting an exponential curve to the system response data points, the equation fit to the data points in FIG. 4 is y=0.6732e^0.1725x, with R² value of R²=0.9652. The optimal range determined using the model illustrated in FIG. 4 is illustrated with a shaded rectangle. The optimal range determined based upon the model illustrated in FIG. 4 is from a load of 900 users to a load of 1,050 users. Once again, it should be noted that the optimal range determined in FIG. 4 corresponds well with the optimal range in FIG. 1, FIG. 2 and FIG. 3.

[0033] One skilled in the art will appreciate that a relationship between units of response time and units of throughput were defined to enable response time and throughput to be illustrated on a single graph in FIGS. 1-4, as well as to allow a distance between the curves to be defined. One skilled in the art will appreciate that the precise relationship between throughput and response time vary depending upon the system in question and the units used to measure throughput and response time, or whatever other network operation parameters are measured and modeled.

[0034] Graphs such as the graph illustrated in FIG. 2 may be particularly useful in accordance with the present invention to visually represent a model of system behavior to a user. The relationship between response time and throughput may also be defined mathematically, for example based upon the distance between the two curves. In this case, the distance may be defined as the value of the throughput curve at a given load minus the value of the response time curve at the same load. The load at which this distance is maximized may be thought of as the optimal system load. It should be noted, however, that an optimal system load may be defined in a variety of ways, some of which are described below in connection with examples of methods in accordance with the present invention. A range around such an optimal system load may be identified as the optimal operating range for a system. This range may be determined, for example, as the range of loads over which the distance between the curves is a given portion, such as ninety percent, of the maximum distance. A system may be monitored and, if its load or the other measured system operating parameters exceed the optimal range, an alarm may be issued so that system operators may take appropriate steps to bring additional resources to the system or to otherwise improve system performance before system performance. Alternatively, the methods in accordance herein may be used to identify a load at which a particular parameter, such as system response time, will cease to be acceptable, and then issue an alarm when that load is reached. Likewise, in some systems other parameters, such as throughput or response time, rather than load, may be monitored with alarms being issued whenever certain threshold values are reached.

[0035] Referring now to FIG. 5, a method 500 for modeling system performance is illustrated. While method 500 illustrates a method in accordance with the present invention for modeling system response time and throughput, methods in accordance with the present invention may be used to model other network parameters. In step 512 the system's throughput may be tested using a plurality of loads to obtain a plurality of throughput data points. Any testing procedure may be used in step 512. The throughput data points obtained in step 512 are utilized in step 516 to model the system's throughput as a function of load by fitting a non-linear curve to the plurality of throughput data points. It should be appreciated that the type of non-linear curve fit to the throughput data points may vary, but may appropriately comprise a logarithmic curve. It should further be appreciated that a variety of methods may be used to fit a non-linear curve to the throughput data points. Furthermore, the number of throughput data points used to fit a curve may vary. For example, a very small number of data points, such as three, may be used in fitting a non-linear curve. Alternatively, a larger number, such as ten as illustrated in FIG. 4, or more may be used to fit a non-linear curve. One skilled in the art will realize that as the number of throughput data points increases the resulting curve will likely better model actual system performance and that a compromise between accuracy of modeling and expense of testing must sometimes be reached. However, methods in accordance with the present invention permit a more favorable compromise to be reached, in that an acceptably accurate model may be achieved using relatively few data points.

[0036] In step 514 the system's response time may be tested using a plurality of loads to obtain a plurality of response time data points. Any testing procedure may be used in step 514. step 514 may be performed in conjunction with step 512, although these steps may also be performed separately. The response time data points obtained in step 514 are utilized in step 518 to model the system's response time as a function of load by fitting a non-linear curve to the plurality of response time data points. It should be appreciated that the type of non-linear curve fit to the response time data points may vary, but may appropriately comprise an exponential curve. It should be further appreciated that a variety of methods may be used to fit a non-linear curve to the response time data points. Alternatively, a larger number of data points, such as five, or seven as illustrated in FIG. 2, or more may also be used to fit a non-linear curve. One skilled in the art will realize that as the number of response time data points increases the resulting curve will likely better model actual system performance and that a compromise between accuracy of modeling and expense of testing must sometimes be reached. However, methods in accordance with the present invention permit a more favorable compromise to be reached, in that an acceptably accurate model may be achieved using relatively few data points.

[0037] In step 520 a maximum acceptable response time may be defined. For example, users of the system may determine that a response time greater than a given predetermined amount, such as five seconds, is unacceptable. Thus, in this example, the maximum acceptable response time would be five seconds. Using the non linear curve modeling the system's response time as a function of load, step 522 may determine the maximum system load as the load at which the maximum acceptable response time is reached. Step 524 may then determine the maximum system throughput using the model of the system's throughput to determine the throughput for the system at the maximum load. Step 520, step 522, and step 524 allow a system's operator to obtain information regarding the maximum capabilities of the system in operation. However, one or more of step 520, step 522, and step 524 may be omitted from methods in accordance with the present invention.

[0038] In step 530 a linear relationship may be defined between response time and throughput. This definition in step 530 may be used in step 532 of displaying a graph of the throughput model curve and the response time model curve in a single graph. If step 530 is omitted, step 532, if performed may display multiple graphs.

[0039] Step 530 may further permit step 534 of calculating the distance between the throughput model curve and the response time model curve. This distance may be monitored in system operation and an alarm may be issued if the distance falls below a threshold amount. The distance calculated in step 534 may be used in step 536 to determine the optimal load for the system. For example, the optimal load may be the load at which the distance between the curves is maximized. Optimal load may be defined in other ways, as well, such as the load at which a desired throughput or response time is attained or the load of which system utilization is reached. An optimal range may be defined around the optimal load for use in monitoring system performance and issuing an alarm should system performance exceed the optimal range.

[0040] Referring now to FIG. 6, an alternative method 600 in accordance with the present invention is illustrated. While method 600 illustrates a method in accordance with the present invention for modeling system response time and throughput, methods in accordance with the present invention may be used to model other network parameters. Method 600 may be particularly suitable for modeling the performance of a system already functioning in a production environment. In step 612 the system's throughput is measured at predetermined times to obtain throughput data points at the loads existing at the measurement times. The predetermined times at which measurements are taken according to step 612 may appropriately vary from system to system. For example, measurements could be made on a daily, hourly, weekly, or other basis. Measurements could also be made after a predetermined number of system processes. In step 613 the throughput data points may be stored. Step 613 may store the throughput data points to a hard drive, in computer memory, or in any other fashion. In step 616 the throughput data points may be used to model the system's throughput as a function of load by fitting a non-linear curve to the stored system throughput data points. It should be noted that a variety of non-linear curves may be used, such as a logarithmic curve. One skilled in the art will realize that a variety of curve-fitting methodologies may be used. It should be further noted that step 616 may be performed at a variety of times. For example, step 616 may be performed at predetermined times, such as on a daily or weekly basis. Alternatively, step 616 may be performed every time a predetermined number of new throughput data points have been stored in step 613. For example, step 616 may be performed one, ten, on hundred, or some other number of new data points have been stored in step 613. Whatever timing is used to perform step 616, it may be expected that as additional throughput data points are added the curve modeled in step 616 will increasingly and accurately reflect system throughput as a function of load. Step 616 may use every stored throughput data point, or it may use a subset of stored throughput data points, such as the data points for the last week of operation.

[0041] In step 614 the system's response time is measured at predetermined times to obtain response time at the loads existing at the measurement times. As with step 612, step 614 may be performed at a variety of times, such as on a daily, hourly, weekly, or other basis as appropriate for the system in question. In step 615 the response time data points may be stored. Step 615 may store the response time data points to a hard drive, in computer memory, or in any other fashion. In step 618 the response time data points may be used to model the system's response time as a function of load by fitting a non-linear curve to the stored system response data points. It should be noted that a variety of non-linear curves may be used, such as an exponential curve. One skilled in the art will realize that a variety of curve fitting methodologies may be used. It should be further noted that step 618 may be performed at a variety of times. For example, step 618 may be performed at predetermined times, such as on a daily or weekly basis. Alternatively, step 618 may be performed every time a predetermined number of new response time data points have been stored in step 615. Fore example, step 618 may be performed when one, ten, one hundred, or some other number of new data points have been stored in step 615. Whatever timing is used to perform step 616, it may be expected that as additional response time data points are added the curve modeled in step 618 will increasingly and accurately reflect system response time as a function of load. Step 618 may use every stored response time data point, or it may use a subset of stored response time data points, such as the data points for the last week of operation.

[0042] In step 620 a maximum acceptable response time may be defined. The maximum acceptable response time may be a predetermined amount of time within which a response must be made by the system for system performance to be deemed acceptable. For example, a maximum acceptable response time of five seconds may be used. If system response time is being monitored step 621 may issue an alarm if the maximum acceptable response time is reached or exceeded. Such an alarm may indicate that the system requires additional resources or adjustments to function properly. Alternatively, step 621 may issue an alarm when response time reaches a predetermined percentage of the maximum acceptable response time, such as, for example, eighty percent.

[0043] Based upon the maximum acceptable response time defined in step 620 and the model of the system's response time as a function of load created in step 618, step 622 may determine the maximum system load as the load at which the maximum acceptable response time is reached. In step 623 an alarm may be issued if the maximum system load is reached. Alternatively, step 623 may issue an alarm if a predetermined percentage of the maximum system load is reached, such as, for example, eighty percent. In step 624 the maximum system load determined in step 622 and the model of the system's throughput as a function load created in step 616 may be used to determine the maximum system throughput as the throughput at the maximum system load. In step 625 an alarm may be issued if the maximum acceptable response time is reached. Alternatively, step 625 may issue an alarm if a predetermined percentage of the maximum throughput is reached, for example eighty percent.

[0044] In step 630 a relationship may be defined between response time and throughput. The relationship defined in step 630 may be a linear relationship. In step 632 a graph may be displayed of the throughput model curve and the response time model curve. Step 632 may display both curves in a single graph through a graphical user interface. If step 630 is omitted, step 432 may display the curves in multiple graphs.

[0045] The relationship between throughput and response time defined in step 630 may also be used to calculate a distance between the throughput model curve and the response time model curve in step 634. Using distance as calculated in step 634, step 636 may determine an optimal load as the load at which the distance between the curves is maximized. Optimal load may be defined in other ways, as well, such as the load at which a desired throughput or response time is attained or the load at which a given system utilization is reached. Step 640 may define an optimal load range around the optimal load. In step 641 an alarm may be issued if the optimal load range defined in step 640 is exceeded.

[0046] Of course, methods in accordance with the present invention, such as method 500 and method 600, may be used to model network parameters other than system throughput and system response time. Methods in accordance with the present invention may be used to measure a first network parameter, model the first network parameter as a non-linear curve, measure a second network-parameter, and model the second network parameter as a non-linear curve. Measuring the first network parameter and the second network parameter may comprise testing the system, measuring the network parameters during system operation, or a combination thereof. A relationship may be defined between the first network parameter and the second network parameter. Such a relationship may allow a distance to be determined between the curve modeling the first network parameter and the curve modeling the second network parameter. Such a relationship may also allow the display of the curve modeling the first network parameter and the curve modeling the second network parameter on a single graph.

[0047] It should be appreciated that example method 300 and example method 400 are exemplary methods in accordance with the present invention, and that many steps discussed therein may be omitted, while additional steps may be added. The methods in accordance with the present invention are not limited to any particular way of obtaining data points, whether through testing or monitoring a system in a production environment, nor are they limited to any particular method for fitting a non-linear curve to the data points obtained.

[0048] It should be further realized that a variety of actions may be taken if an alarm is issued in accordance with the present invention for a system in a production environment. Additional computing resources may be added, such as additional servers or additional system memory, the software of the system may be improved and modified to enhance efficiency, or some combination of the two may be taken. Alternatively, steps may be taken to reduce load on the system to facilitate better system performance. The steps taken by a system operator due to information obtained in practicing methods in accordance with the present invention are immaterial.

[0049] Referring now to FIG. 7, a method 700 for monitoring and modeling system performance in a production environment is illustrated. System metrics are defined in step 705. any measurable system metric may be defined in step 705, and one skilled in the art will appreciate that different system metrics may be appropriate for different systems. In step 710 system metrics are recorded. Recording step 710 may be performed in a variety of ways. For example, a system to be monitored may periodically record metrics associated with that system and maintain it on storage media associated with that system for later collection and/or analysis. One skilled in the art will be familiar with the use of monitoring software that operates on a functioning system to periodically record system metrics. Recording step 710 may also be performed external to the system to be monitored. In step 715 system metrics are collected. Collecting step 715 and recording step 710 may be combined, particularly if recording step 710 occurs external to the system to be monitored. If recording step 710 occurs on the storage media of the system to be monitored, collecting step 715 may occur less frequently than recording step 710. For example, recording step 710 may occur hourly, while collection step 715 may occur once daily. Collection step 715 may serve to transport metric data to an external system for analysis and modeling. One skilled in the art will appreciate that collection step 715 may be advantageously scheduled to occur during times, such as overnight, when system and bandwidth resources are not in high demand.

[0050] The system metric data may be analyzed to identify correlations between the system metrics in identification step 720. Identification step 720 may be particularly advantageous when a large number of metrics are measured, not all of which have known correlations between them. In identification step 720 various metrics data may be analyzed over a given period of time to determine whether a mathematical relationship exists between a pair of metrics, such as system load and processor utilization. The identification of correlations between system metrics may then be used to provide more accurate models of system performance.

[0051] In step 725 system performance is modeled as a non-linear relationship between system metrics. The model constructed in modeling step 725 may utilize correlations identified in identification step 720 or may use predetermined metrics identified by a system administrator or others through prior experience.

[0052] In step 730 unacceptable system operation ranges may be identified. For example, a model constructed in modeling step 725 may indicate that a certain monitored system metric, such as system response time, may reach an unacceptable range when another system metric, such as system load, reaches a given point. Step 730 may identify a variety of unacceptable system operation ranges for each pair of metrics modeled, and may further identify unacceptable operation ranges for more than one pair of metrics. For example, varying degrees of unacceptable system response time may be identified. The degree to which each identified range is unacceptable may increase, from a moderately unacceptable level that requires prompt attention to correct to a fully unacceptable response time which requires immediate corrective action.

[0053] In step 735 an optimal system operation range may be identified using the model constructed in modeling step 725. Methods such as those described above that maximize the distance between curved modeling to different metrics to as a function of load may be used to identify an optimal system operation range in step 735.

[0054] Alarm thresholds may be defined in step 740. The alarm thresholds defined in step 740 may be based upon one or more unacceptable system operation ranges identified in step 730 and/or an optimal system operation range identified in step 735. The alarms defined in step 740 may constitute varying degrees and may be based upon different metrics. For example, an alarm may be defined to trigger if system metrics leave the optimal system operation range defined in step 735. Such an alarm may be of a low level, given that the performance of the monitored system may be non-optimal but may remain entirely acceptable to users. A higher level of alarm may then issue if one or more system metric enters into an unacceptable system operation range. If varying degrees of unacceptable system operation ranges were identified in step 730, correspondingly differing alarms may be defined in step 740.

[0055] In step 745 alarms may be issued if alarm thresholds are met. Step 745 may operate based upon the alarm thresholds defined in step 740 and the system metrics collected in step 715. Alternatively, a system may periodically receive alarm thresholds defined in step 740 and may issue an alarm if the systems recorded metrics recorded in step 710 meet or exceed an alarm threshold.

[0056] Referring now to FIG. 8, an environment in which systems and methods in accordance with the present invention for monitoring and modeling system operation in a production environment is illustrated. A server 810 or other appropriate computing equipment may operate software implementing methods in accordance with the present invention. Any number of systems may be monitored and modeled in accordance with the present invention, including computer software operating on a first server 820, computer software operating on a second server 830 and computer software operating on a third server 840. Monitored systems in accordance with the present invention may be related, but need not be related. Monitored systems may include multiple systems that utilize a single server. Server 810 performing monitoring and modeling functions may connect to the servers for systems to be monitored and modeled through any networking means, such as through a local area network. Collected metrics from the monitored systems may be stored in databases 895. Databases 895 may comprise a single database or may comprise multiple databases. Databases 895 may be maintained upon server 810 or may be maintained external to server 810. Server 810 and the software implementing systems and methods in accordance with the present invention for monitoring and modeling systems may be accessed through client 815. Client 815 may be any device through which a system operator may access and/or manipulate the software in accordance with the present invention operating upon server 810 and/or the system metrics collected in accordance with the present invention. Client 815 may be a computer workstation, a desktop computer, a lap top personal computer, a PDA, mobile telephone, table personal computer, or any other computing device. Server 810 may also connect through any appropriate networking media to devices for use in issuing system performance alarms. For example, an alarm may be sent to a system administrator's computer 850 through e-mail, instant messaging, or other means. By way of further example, an alarm may be sent to a monitoring server 860 and placed in a queue for access by a system administrator. As a yet further example, an alarm may be transmitted to mobile phone 870 belonging to a system administrator by use of a recorded message, a short text message, or other means. As a further example, an audible alarm 880 may be used to audibly notify a system administrator or other personnel of the status of a monitored system.

[0057] Referring now to FIG. 9, a system 900 for collecting system metrics is illustrated. While systems in accordance with the present invention, such as system 900, may operate with any commercially available system monitoring software, or may further operation with specially developed system monitoring software, the example of system 900 illustrated in FIG. 9 is particularly adapted for use with commercially available monitoring software sold under the trademark MeasureWare. One skilled in the art will appreciate the modifications that could be made to system 900 to allow operation with other monitoring software.

[0058] System 900 includes component 910. Component 910 includes a log adaptor 912. Log adaptor 912 may operate on a server on other computing device and may execute process in accordance with software implementing the methods of the present invention. Log adapter 912 may relay upon manually created configuration files 915 in operation. Manually generated files 915 may include a DSI configuration file 916. The DSI configuration file 916 may comprise lines describing the type-delimited metrics to collect, the type-delimited metric format, the path to the log set, the value at which to trigger an alarm or a metric (which may be left blank to turn of alarming), the application name, the application designation, the open view OPC message group, to indicate whether DSI logging is on or off, and settings for specific files such as the maximum indexes per record per hour per summarization or per retention. Manually generated files 915 may further comprise an IDS configuration file 917 to set to initial type-delimited values, the first being the starting transaction ID number and the second being the starting metric ID number to use when generating new specification files. Manually generated files may further include the OBCA client configuration file 918.

[0059] Automatically generated files 920 may also be part of system 910. Automatically generated files 920 may include a class configuration file 921 that contains one line per transaction with the short transaction name from the transaction configuration file 922. Transaction configuration file 922 may be a translation file to accommodate the 18-character limit in MeasureWare log file set names. Each line of the translation configuration file 922 may contain one line per transaction that has two values that are type-delimited. The first value may be a potentially shortened value of the full transaction name that is within the 18-character maximum followed by the full transaction name. The long name of a transaction may be the name actually used for a log file, with the short name being used in the class configuration file 921 for use by MeasureWare. The time threshold configuration file 923 may hold the average service time threshold violation values per transaction for levels of warnings such as minor, major, critical, or other levels desired by a user. An error threshold configuration file 924 may also be used, but may be omitted. A percent threshold configuration file 925 also may be optionally included. A previous alarm configuration file 926 may be included to maintain historical alarm information.

[0060] Log adapter 912 may receive manually generated files 915 and may operate in to generate the automatically generated files 920. Log adapter 912 a further interface with software component 955 and database interface 970. Systems metric measurements may be entered into database system 980 through interface 970. Database system 980 may comprise one or more databases, for example, as illustrated in FIG. 9, a first database 981, a second database 982, and a third database 983, although one skilled in the art will appreciate that the number and type of database used may vary. Log adapter 912 may execute to create specification files 930. Log adapter 912 may further execute a log file process 940 to create a log file set 945. Log file set 945 may further be generated from a composition 935 executed by log adapter 912 using spec files 930. Log files may be accessed by performance management software 960 through server 950.

[0061] Referring now to FIG. 10, a method 1000 illustrating a log adapter processes in accordance with the present invention is illustrated. Method 1000 begins with receiving initial inputs in step 1010. Based upon the initial inputs, the log adapter processes is built in step 1015. The log adapter process is then distributed to the system in step 1020. The log adapter process running on system is monitored in step 1025. Based upon step 1025, in step 1030 it is determined whether the log adapter process is running in all systems. If the result of step 1030 is that the log adapter process is not running on all systems, method 1000 proceeds to step 1035 were in the log adapter process is restarted on systems wherein it is not running. If the result of step 1030 is that the log adapter process is running in all systems, method 1000 proceeds to step 1040 of generating configuration files for metric data. In step 1045 metrics to be collected are defined. One skilled in the art will note that step 1045 may be performed by a human operator, as part of an automated process, or some combination of a human operator and an automated process. Method 1000 may then proceed to set thresholds for data collection in step 1050 and for alarming in step 1055. Thresholds may be set by human operator, by an automated process, or some combination of both. One skilled in the art will appreciate that step 1050 of setting a threshold for data collection may be omitted if it is desired for data collection to always be conducted. Similarly, step 1055 of setting thresholds for alarming may be omitted if no alarming function is desired. Step 1055 of setting thresholds for triggering alarms may involve substeps based upon differing levels of alarms. In such an implementation, thresholds may be set for varying degrees of alarms such, for example, a warning alarm in step 1090, a minor alarm in step 1091, a major alarm in step 1092, and a critical alarm in step 1093. Of course, one skilled in the art will appreciate that the number and variety of alarms may vary from that described herein without departing from the spirit and scope of the present invention. Method 1000 may proceed to step 1060 of monitoring applications running on a system. In step 1065, metrics are collected from applications. In step 1070 the collected metrics are placed in a file. Method 1000 may then proceed to a check of the log adapter process in step 1075, and may then proceed to step 1035 of restarting the log adapter process if the log adapter process is not running on a system.

[0062] In step 1080 system performance may be modeled using the collective data. One skilled in the art will appreciate that any of the methods described herein may be used in step 1080, and further that other methods of modeling system performance may likewise be used. In step 1085 system performance may be measured against threshold values. The results of step 1080 and step 1085 may then be used to redefine metrics to be collected and/or thresholds in steps 1045, 1050, and 1055.

[0063] Referring now to FIG. 11, an implementation of the present invention on a plurality of systems is illustrated. A first system to be monitored 1110 has monitoring software 1112 operating upon it. First system 1110 is linked to network 1140 by connection 1115.

[0064] With a second system to be monitored 1120 has monitoring software 1122 operating upon it. Second system 1120 is connected to network 1140 by connection 1125.

[0065] A third system to be monitored 1130 has monitoring software 1132 installed upon it. Third system 1130 is connected to network 1140 by connection 1135.

[0066] One skilled in the art will appreciate that network 1140 may be any of a variety of networks permitting data communication, such as an internet protocol (IP) network. Depending upon the type of network used for network 1140, connections through network 1140 may take varies forms such, as logical connections or physical connections. A metric monitoring system 1150 may operate to collect metrics from the various systems to be monitored. For example, monitoring system 1150 may be connected to network 1140 by connection 1155. System metrics measured by system monitoring software 1112 operating on the first system to be monitored 1110 may be reported to monitoring system 1150 through a first network connection 1141. In a similar fashion, system monitoring software 1122 operating upon the second system to be monitored 1120 may report measured system metrics through network connection 1142. likewise, system monitoring software 1132 operating upon the third system to be monitored 1130 may report system metrics to monitoring system 1150 through network connection 1143. Monitoring system 1150 may store collected system metrics in database 1170 connected to network 1140 by connection 1175 through network connection 1147. Alternatively, monitoring system 1150 may retain collected system metrics itself. A log adapter system 1160 connected to network 1140 by connection 1165 may access collected system metrics to convert the collected metrics into a format more suitable for use in modeling or other methodologies in accordance with the present invention. If database 1170 is used to store collected system metrics log adapter 1160 may access database 1170 through network connection 1148. Log adapter 1160 may access monitoring system 1150 through network connection 1146 to retrieve system metrics maintained by monitoring system 1150 and/or to modify parameters of metric collection. Log adapter system 1160 may store converted system metrics in second database 1180 through network connection 1149. Second database 1180 may be connected to network 1140 by connection 1185. The converted system metrics stored in second database 1180 may be accessed by system modeling system 1190 through network connection 1191. Modeling system 1190 may connect to network 1140 by connection 1195. Modeling system 1190 may use methods such as described previously herein to model system performance based upon collected system metrics.

[0067] Referring now to FIG. 12, a further method 1200 in accordance with the present invention is illustrated. Method 1200 may begin with measuring system metrics in step 1210. Step 1210 may be performed, for example, by system monitoring software operating upon a system to be monitored. Method 1200 may proceed to step 1220 of reporting system metrics. Step 1220 may occur, for example, when system monitoring software operating on a system to be monitored reports measured system metrics over a network to a monitoring system. In step 1230 the system metrics may be stored. Step 1230 may occur, for example, on the monitoring system when system metrics are reported. In step 1240 system metrics are exported to a database. Step 1240 may occur, for example, when system metrics are exported to a database over a data network. In step 1250 system metrics may be analyzed to identify correlations. Any method may be used to identify correlations, including methods described previously herein.

[0068] Referring now to FIG. 13, a further method in accordance with the present invention is illustrated. In step 1310 system metrics are measured in a first format. Step 1310 may be performed, for example, by system monitoring software operating on a system to be monitored. In step 1390 the measured system metrics are converted to a second format. Step 1390 may be performed by system monitoring software operating upon a system to be monitored, or may be performed by an external system such as a log adapter system. In step 1320 system metrics may be reported in the second format. In step 1330, the system may be stored in the second format. In step 1340, the system metrics may be exported to a database in the second format. In step 1350, the system metrics may be analyzed to identify correlations. One skilled in the art will appreciate that step 1350 may be performed using any methodology, including the methodologies previously described herein.

[0069] Referring now to FIG. 14, a further method 1400 in accordance with the present invention is illustrated. In step 1410 system metrics are measured in a first format. Step 1410 may be performed by system monitoring software operating on a system to be monitored. In step 1420 system metrics are reported in a first format. Step 1420 may be performed, for example, by system monitoring software reporting system metrics to a monitoring system over a network. In step 1430 system metrics are stored in the first format. Step 1430 may be performed, for example, at the monitoring system. In step 1490 system metrics are converted from the first format to a second format. Step 1490 may occur, for example, on the monitoring system or may be performed by external systems such as a log adapter system. In step 1440 system metrics are exported in a database in a second format. In step 1450 system metrics are analyzed to identified correlations. Methods such as those previously describe herein may be used to perform step 1450, although other methods may be used as well.

[0070] Referring now to FIG. 15, a further method 1500 in accordance with the present invention is illustrated. In step 1510 system metrics are measured in a first format. Step 1510 may be performed, for example, by system monitoring software operating on a system to be monitored. In step 1520 system metrics are reported in a first format. Step 1520 may be performed, for example, by transmitting system metrics from system monitoring software over a network to a monitoring system. In step 1530 system metrics are stored in a first format. Step 1530 may occur, for example, at a monitoring system. In step 1540 system metrics may be exported to a database in a first format. Step 1540 may be performed, for example, by exporting system metrics form a monitoring system over a network to a database. In step 1590 system metrics may be converted from the first format to a second format. Step 1590 may be performed, for example, by a system such as a log adapter system that accesses metrics stored in a database in the first format. In step 1550 system metrics may be analyzed to identify correlations. Methods such as those previously described herein may be used to perform step 1550, although any other method may be used as well.

[0071] One skilled in the art will appreciate that methods in accordance with the present invention may be implemented using computer software. Such software may take the form of computer readable code embodied on one or more computer readable media. Software implementing the present invention may operate independently, but may also be incorporated with system testing software or system monitoring software. Any software language may be used to implement methods in accordance with the present invention.