Selecting metrics for system monitoring

Ranking system metrics for monitoring by sorting members of a set of system metrics into correlation groups according to correlations among historic time series data, determining a sensitivity of the members of the set of system metrics, determining an importance of the members of the set of system metrics according to the correlation groups and sensitivity, and ranking the members of the set of system metrics according to the importance.

BACKGROUND

The disclosure relates generally to computer-based hierarchical analysis of system metrics. The disclosure relates particularly to ranking system metrics for real-time system monitoring and analysis according to domain expertise, statistical and machine learning analysis of system metric time series data, and the desired number of metrics for monitoring.

In IT operation domains, health care systems, electricity generation and distribution systems, and Internet of Things systems there are efforts to monitor and display real time performance for hundreds of subsystems, and thousands of data metrics. A system such as a DB2z, database management system on a mainframe, collects thousands of metrics each minute, and thousands of records for hundreds of metrics every second. Such systems generate and collect huge volumes of time series data.

SUMMARY

Aspects of the invention disclose methods, systems and computer readable media associated with ranking system metrics for monitoring by sorting members of a set of system metrics into correlation groups according to correlations among historic time series data of the system metrics, determining a sensitivity of the members of the set of system metrics, determining an importance of the members of the set of system metrics according to the correlation groups and sensitivity, and ranking the members of the set of system metrics according to the importance.

DETAILED DESCRIPTION

In an embodiment, one or more components of the system can employ hardware and/or software to solve problems that are highly technical in nature (e.g., grouping system metrics according to correlation levels, determining sensitive features, determining relative importance of system metrics according to sensitive features and correlation groupings, selecting the N most important metrics for monitoring or analysis, etc.). These solutions are not abstract and cannot be performed as a set of mental acts by a human due to the processing capabilities needed to facilitate system metrics to be monitored, for example. Further, some of the processes performed may be performed by a specialized computer for carrying out defined tasks related to system metric selection. For example, a specialized computer can be employed to carry out tasks related to selecting system metrics for monitoring and analysis or the like.

Extensive systems, including hundreds or thousands of data collection points and the associated millions of data records, represent vast collections of raw data. As an example, a health care data base may include thousands of different data fields relating to individual records and system performance measures. Similarly, a big data set associated with an extensive IoT system may include thousands of unique data metrics associated with individual sensors as well as system performance measures. Such systems may rely exclusively upon domain experts to select metrics for monitoring and analysis.

Identifying selected data metrics from among the thousands of raw data metrics for close monitoring enables judicious use of scarce monitoring resources to effectively capture data anomalies indicative of system issues. Selecting appropriate data metrics further enables system performance monitoring and fault analysis without a resource and cost prohibitive requirement that all metrics be monitored. Disclosed systems and methods enable the automated selection of important system metrics, specific to each system and most significant to the monitoring and analysis of system performance. Such automated selection enables effective monitoring of system performance without excessive resource use or the monitoring of redundant metrics.

In an embodiment, the method receives system metric rankings of system key performance indicators (metrics) according to system metric functional areas, from a system domain expert. The method performs statistical analyses of historic time series data to group system metrics according to the level of correlation between metrics. The method further considers lag correlation—analyzing pairs of metrics for correlation after compensating for a lagging response between the pair of metrics. The method identifies anomaly patterns in the historic data as well as system metric status changes. The method identifies status changes as normal or abnormal. The method groups metrics according to the normal/abnormal classifications. The method utilizes a feature selection algorithm to identify the top N metrics from the abnormal group.

The method combines the domain expert ranking, correlation grouping by statistical analysis of metric time-series data, and machine learning-based anomaly identification of metrics and preforms a combinatorial analysis of the combined data to select the top N metrics from the overall set of metrics. The method may weigh the data according to the various data sources and further in terms of a user defined number of desired metrics for the output.

In an embodiment, the method receives input from one or more domain experts regarding the classification and relative importance of the system metrics. In this embodiment, a domain expert divides the overall set of available system metrics into categories using system metric descriptions. The categories are associated with the system metric functions as set forth in the system metric descriptions. In this embodiment, the domain expert further rates each metric according to how well known or important the expert considers the system metric to be, creating a set of ranked tiers of the system metrics. For example, the expert may rate: (i) system metrics considered important as 1 rated, or critical, (ii) “nice to have” metrics as 0.8 rated, and (iii) all other metrics as 0.5 rated, or “normal”. The output from the domain expert includes a ranked tiering or hierarchical tree having system metrics grouped by functional area and also relative importance.

In an embodiment, the method may categorize a set of ten system metrics, K1-K10, as function 1, F1:[K1, K2, K3], having ranked values of [1, 0.8, 0.3], F2: [K4, K5], having ranked values of [0.8, 0.3], F3:[K6, K7, K8, K9], having ranked values of [1, 0.8, 0.3, 0.3], and F4: [K10], having a ranked value of [1]. In this embodiment, the categories are determined according to the expertise of the domain expert.

In an embodiment, the method performs a statistical correlation analysis of the system metric time series data. In this embodiment, the method calculates the correlation between each possible pair of metrics as a vector. The method further calculates the lag correlation between each pair with consideration of time shifts between the time-series data for the pair. The method progressively shifts the time-series data of one metric of the pair while calculating the pair's correlation after each shift to determine the time shift yielding the greatest correlation value for the pair. As an example, the method calculates the correlation between every 2 KPI values in the time series data as a vector. The method then shifts the time interval between the time series data of the two KPIs and calculates the correlation on the time shifted data. In an embodiment, the method calculates correlation for a time lag of zero up to a time lag of twenty, time intervals. The time interval may be selected according to the rate of the change of the KPIs over time—selecting an interval over which the KPIs change. The method outputs groups of metric pairings having similar levels of direct or lagging correlation. The method targets group sizes for correlated metrics of between N/2 and N, for a desired set of N system metrics. System metrics in a single group based upon direct or lagging correlation may be redundant in terms of system monitoring and present an opportunity to reduce the number of monitoring metrics required to analyze system performance. Only a single metric from a group of highly correlated metrics need be monitored. In an embodiment, the method utilizes a correlation algorithm such as the Pearson correlation coefficient or a similar algorithm.

In an embodiment, the method conducts machine learning-based anomaly analysis of system metric time-series data. In this embodiment, the method uses a machine learning model, such as a Gaussian model, or a Denoise autoencoder, to identify anomalous data. The method may consider directly reported data anomaly time ranges within the time-series data or may evaluate the data directly without reported anomalies to detect anomalous data time ranges. The method further analyzes the time-series data to identify system metric status change time ranges. The method may utilize reported metric status change time ranges or evaluate all the time-series data to detect status changes without using reported status change time ranges. The method utilizes a clustering algorithm such as k-means clustering, to group the time series data into two or more clusters for each reported or identified metric status change time range. The method labels the grouped time-series data as “0”, or “1” according to the status of the data as normal/anomalous, or idle/busy, for the identified time ranges. For each identified anomaly or status change, the method outputs those metrics having the greatest sensitivity to the changes or anomalies according to a feature selection algorithm such as a decision tree or random forest algorithm. The method evaluates the system metric time-series data in terms of different anomaly types using algorithms such as isolated forest, dbscan, or an autoencoder, to identify anomalies in the time-series data. In an embodiment, the method imports pre-defined anomaly type definitions.

For each factor described, the method evaluates each system metric as either selected, or not selected. Selected metrics include those ranked as having high importance by a domain expert, those having high correlation, and those having a sensitivity to anomaly and system status changes.

In an embodiment, the method combines system metric correlation data and anomaly detection data for sensitive metrics. In one embodiment, the method further combines the domain expert system metric classification and ranking data. The method applies an integer linear optimization model to the combined input data. The method applies an initial weighting value to each of the correlation and anomaly data sets, as well as the domain expert data set when combining the correlation and anomaly data with such a data set. The method may further consider a desired number of important system metrics, N, as a fourth weighted parameter for the integer linear optimization model. The method runs the optimization model and evaluates the solution according to the desired metric number as well as desired coverage levels for the hierarchical tree of domain expert input, the correlation groupings, and the anomaly groupings. In an embodiment, the method receives user input regarding desired coverage levels for each input data set, or the method utilizes default coverage levels for each input set. The method considers the coverage of the selected metrics in terms of the sets of groupings determined for each input set. For embodiments without a defined N, the method seeks to optimize the constraints of each utilized input data set.

In an embodiment, input data set constraints include requiring the selection of at least one metric from each domain expert functional group, the selection of at least one metric from each for anomaly/status change grouping according to metric importance, and the selection of the desired number of metrics.

In an embodiment, the method considers objective equations for each of the factors. For the number of desired metrics factor, the method considers the number of selected metrics divided by the number of desired metrics. For the domain expert input factor the method considers the sum of the domain expert ranked importance values for each selected factor divided by the number of functional groupings. For the correlation input set, the method considers the number of correlation groups represented in the selected metrics divided by the total number of correlation groups. For the anomaly sensitivity set the method considers the sum of the selected metrics anomaly importance divided by the total number of anomalies.

Optimization considers and seeks to maximize the sum of the objective equations multiplied by the weighting for each factor considered from among the four possible factors (domain expert classification, correlation grouping, anomaly status change grouping, and desired number of metrics.) For each factor the method determines the objective calculation value and multiplies this value by the current weighting value for the factor. The factor weighting values sum to 1.

Exemplary factor weights relate to each of the four factors. The method utilizes a set W of weights w(0), w(1), w(2), w(3) for max metrics count, metrics functional importance, coverage on correlation group and importance on anomaly detection, respectively. For w(0)E[0,1], the weight for metrics selected count limitation, a heavier weight will force fewer metrics to be selected. For w(1) E[0,1], the weight for domain expert functional importance, increasing the weight enables more important metrics in functional areas to be selected. For w(2) E[0,1], the weight for correlation group coverage percent, a greater weight will result in metrics included in more groups being selected. For w(3) E[0,1], the weight for anomaly detection/status change, a greater weight will select metrics with more importance or the selection of more metrics from the anomaly detection/status change list.

In an embodiment, an automatic configuration to check the count of anomaly cases in historical data, for an anomaly frequency over a thresholder such as 1/10000, the method will set W=(0.1, 0.1, 0.1, 0.7) to select more anomaly sensitive metrics. The selected metrics help to detect anomalies early in further system monitoring/analysis. Otherwise, identical weights of (0.25, 0.25, 0.25, 0.25) help to select a balanced metrics list. A balanced metrics listing covers the aspects of the set of system metrics equally without emphasis or preference for any particular system aspect.

In an embodiment, for monitoring in real time, the method utilizes W=(0.5, 0.3, 0.1, 0.1) to select a metrics list focused more on functional importance in a relatively small count N of metrics.

In an embodiment, for analysis of alert anomalies, the method utilizes W=(0.1, 0.1, 0.3, 0.5) to enable the selection of more sensitive metrics for anomaly detection and to cover more diverse groups on correlation.

In an embodiment, the method defines equal initial weightings for each input data set provided to the integer linear optimization model. The method may receive initial weightings from a user, or weighting adjustments from the user in response to an initial optimization model solution and a user desire to alter the solution through the weightings.

In response to the optimization results, the method ranks the system metrics according to the importance values from the optimization. The method may provide the desired number of ranked metrics N, provided by a user, or the method may derive a ranked number of system metrics according to the relative importance, using all metrics above a selected metric importance threshold, such as 0.8, 0.6, etc. The user may provide a threshold or may provide the explicit number N of desired system metrics. Absent user input regarding N, the method ranks metrics without regard to the N factor, effectively setting the weighting for this factor as zero.

FIG. 1provides a schematic illustration of exemplary network resources associated with practicing the disclosed inventions. The inventions may be practiced in the processors of any of the disclosed elements which process an instruction stream. As shown in the figure, a networked Client device110connects wirelessly to server sub-system102. Client device104connects wirelessly to server sub-system102via network114. Client devices104and110comprise application program (not shown) together with sufficient computing resource (processor, memory, network communications hardware) to execute the program. As shown inFIG. 1, server sub-system102comprises a server computer150.FIG. 1depicts a block diagram of components of server computer150within a networked computer system1000, in accordance with an embodiment of the present invention. It should be appreciated thatFIG. 1provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made.

Server computer150can include processor(s)154, memory158, persistent storage170, communications unit152, input/output (I/O) interface(s)156and communications fabric140. Communications fabric140provides communications between cache162, memory158, persistent storage170, communications unit152, and input/output (I/O) interface(s)156. Communications fabric140can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric140can be implemented with one or more buses.

Memory158and persistent storage170are computer readable storage media. In this embodiment, memory158includes random access memory (RAM)160. In general, memory158can include any suitable volatile or non-volatile computer readable storage media. Cache162is a fast memory that enhances the performance of processor(s)154by holding recently accessed data, and data near recently accessed data, from memory158.

Program instructions and data used to practice embodiments of the present invention, e.g., the system metric ranking program175, are stored in persistent storage170for execution and/or access by one or more of the respective processor(s)154of server computer150via cache162. In this embodiment, persistent storage170includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage170can include a solid-state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.

The media used by persistent storage170may also be removable. For example, a removable hard drive may be used for persistent storage170. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage170.

Communications unit152, in these examples, provides for communications with other data processing systems or devices, including resources of client computing devices104, and110. In these examples, communications unit152includes one or more network interface cards. Communications unit152may provide communications through the use of either or both physical and wireless communications links. Software distribution programs, and other programs and data used for implementation of the present invention, may be downloaded to persistent storage170of server computer150through communications unit152.

I/O interface(s)156allows for input and output of data with other devices that may be connected to server computer150. For example, I/O interface(s)156may provide a connection to external device(s)190such as a keyboard, a keypad, a touch screen, a microphone, a digital camera, and/or some other suitable input device. External device(s)190can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., system metric ranking program175on server computer150, can be stored on such portable computer readable storage media and can be loaded onto persistent storage170via I/O interface(s)156. I/O interface(s)156also connect to a display180.

Display180provides a mechanism to display data to a user and may be, for example, a computer monitor. Display180can also function as a touch screen, such as a display of a tablet computer.

FIG. 2provides a flowchart200, illustrating exemplary activities associated with the practice of the disclosure. After program start, at block210, the method (e.g., program175) receives system metrics grouped by a domain expert according to the functional area of the metric, and further ranked according to relative importance by the system expert. For example, metrics may be ranked as critical, nice to have, or normal metrics.

At block220, the method groups the system metrics according to the degree of correlation between pairs of system metrics over the duration of the respective sets of time-series data for the metrics. The method considers both real-time correlations, how well the pair of metrics correlates along a common timeline, and lag correlations, how well the pair of metrics correlates when the values of one metric of the pair are shifted in time from the values of the other metric of the other pair. The method groups system metrics as highly correlated or not highly correlated. The method considers coverage of the correlation groups with respect to the overall set of system metrics. Groups of highly correlated metrics present an opportunity to reduce the number of monitored metrics by selecting a single metric from the group of highly correlated metrics.

At block230, the method analyzes the system metrics in terms of the sensitivity of the metrics to system status changes and to anomalies in the time series data. The method uses machine learning models such as Gaussian models or autoencoders to detect metrics sensitive to data anomalies and system status changes. The method either utilizes provided status change and anomaly data, including time range data associated with the anomalies and status changes, or the method analyzes the system metric time-series data to identify status changes and anomalies in the time-series data.

At block240, the method optimizes the view of the system metrics according to a combination of the domain expert input, the correlation groupings, the system metric sensitivity analysis, and also the number of desired ranked system metrics for the method output. The optimization yields an output of an importance for each system metric. In an embodiment, the method utilizes an integer linear optimization algorithm for ranking the system metrics in the view. The method considers domain expert input and the desired number of ranked system metrics as optional inputs, the ranked view of the metrics may be derived with or without these inputs. As an example, the method may rank the set of system metrics by optimizing the view of the metrics including only the correlation grouping factor combined with the system metric sensitivity analysis factor.

At block250, the method ranks the members of the set of system metrics according to the importance determined in the optimization. The method provides the ranked system metrics as an output. The method may provide a desired number of ranked metrics as the output, or the method may provide a derived number of system metrics as the output. Monitoring and analysis of the system metrics included in the provided output may proceed through automated or manual systems. As an example, the real-time values of selected metrics may be automatically tracked and compared to metric control limits established for each selected metric. In an embodiment, the control limits are defined by a user such as a system expert. In an embodiment, the control limits may be established according to the typical range of values for each metric in order to identify anomalous values for the metric. In this embodiment, the method includes automated reporting of selected metrics having values outside established control limits.

In an embodiment, the method may include automated response steps associated with defined values for selected metrics. Alarms and other indicators, such as indications of normal or abnormal conditions may be generated using the real-time values of selected metrics.

Disclosed embodiments may be implemented utilizing local computing resources. Disclosed embodiments may also be implemented across networked resources and may benefit by implementation across networked computing environments affording access to edge cloud and cloud computing resources. Such an implementation enables more flexible practice of disclosed methods by allowing the methods to increase or decrease the utilized computing environment resources as needed or possible. Access to networked resources further enables the provision of system metric data sets and the output of ranked system metrics according to disclosed methods across network resources.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG. 4, a set of functional abstraction layers provided by cloud computing environment50(FIG. 3) is shown. It should be understood in advance that the components, layers, and functions shown inFIG. 4are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The invention may be beneficially practiced in any system, single or parallel, which processes an instruction stream. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.