CLOSED LOOP VERIFICATION FOR EVENT GROUPING MECHANISMS

A method and system is provided for utilizing a causal dependence graph of events in a large enterprise-related system to determine a most frequently utilized corrective action for a set of actions that the enterprise requires. Typically, with large sets of data related to actions that an enterprise system performs, it is non-trivial to correlate a set of actions (or workflows) with a set of corrective actions.

BACKGROUND

The present invention generally relates to the field of event grouping, and more specifically to utilizing non-trivial methods to improve event grouping mechanisms for enterprise-related events.

SUMMARY

According to an aspect of the present invention, there is a method, computer program product and/or system that performs the following operations (not necessarily in the following order): (i) receiving a first system state, with the first system state including a plurality of events, and with the plurality of events being included in a first grouping; (ii) determining that the first grouping is non-satisfactory for the first system state; (iii) responsive to the determination, taking a corrective action, by a system engineer, with the corrective action including performing a set of action(s) to a first sub-set of events included in the first grouping; (iv) correlating the set of action(s) to the first sub-set of events with the first sub-set of events to create a causal dependence graph of events; and (v) utilizing the causal dependence graph of events to determine a corrective action of the set of action(s) to determine the most frequently utilized corrective action.

DETAILED DESCRIPTION

Some embodiments of the present invention are directed towards utilizing a causal dependence graph of events in a large enterprise-related system to determine a most frequently utilized corrective action for a set of actions that the enterprise requires. Typically, with large sets of data related to actions that an enterprise system performs, it is non-trivial to correlate a set of actions (or workflows) with a set of corrective actions.

I. The Hardware and Software Environment

An embodiment of a possible hardware and software environment for software and/or methods according to the present invention will now be described in detail with reference to the Figures.FIG.1is a functional block diagram illustrating various portions of networked computers system100, including: server sub-system102; client sub-systems104,106,108,110,112; communication network114; server computer200; communication unit202; processor set204; input/output (I/O) interface set206; memory device208; persistent storage device210; display device212; external device set214; random access memory (RAM) devices230; cache memory device232; and program300.

Memory208and persistent storage210are computer-readable storage media. In general, memory208can include any suitable volatile or non-volatile computer-readable storage media. It is further noted that, now and/or in the near future: (i) external device(s)214may be able to supply, some or all, memory for sub-system102; and/or (ii) devices external to sub-system102may be able to provide memory for sub-system102.

FIG.2shows flowchart250depicting a method according to the present invention.FIG.3shows program300for performing at least some of the method operations of flowchart250. This method and associated software will now be discussed, over the course of the following paragraphs, with extensive reference toFIG.2(for the method operation blocks) andFIG.3(for the software blocks).

Processing begins at operation S255, where receive system state module (“mod”)305receives a first system state. In some embodiments of the present invention, the first system state provides an indication of the overall health of a given computing system at a particular point in time. The system state includes information that shows the processes that are undertaken in a given computing system by looking at a large set of computing events. Typically, these computing events are included in a first grouping (sometimes herein referred to as an “event grouping”).

Processing proceeds to operation S260, where system state evaluation mod310determines that the first grouping of computing events (discussed in connection with operation S255, above) is non-satisfactory. As mentioned above in connection with operation S255, the system state indicates an overall health of the computing system. In order for system state evaluation mod310to determine that the first grouping of computing events is non-satisfactory, mod310must determine that at least some of the events in the event grouping are deficient (that is, at least some events in the event groupings contain a “fault”). A fault typically means that a process that is run as part of an event has failed. This can occur when an event(s) has exceeded a threshold for memory utilization.

Processing proceeds to operation S265, where corrective action mod315takes a corrective action (or multiple corrective actions) for each event included in the first grouping. In some embodiments of the present invention, corrective action mod315first determines what the “fault” in the event(s) is. After determining the fault in the event(s), corrective action mod315suggests to a systems engineer a host of solutions that can potentially alleviate the fault, which the systems engineer can then implement. Alternatively, a systems engineer can independently determine which corrective action to take based upon observing a set of “golden signals” that an event(s) provides. The use of “golden signals” to remedy faults is discussed in greater detail in Sub-Section III, below.

Processing proceeds to operation S270, where correlate actions mod320correlates the corrective actions taken to the set of computing events that have “faults” (discussed in connection with operation S265, above) with a first sub-set of computing events included in the first grouping. In the context of enterprise solutions, correlating a corrective action to a set of problematic (or potentially problematic) events is a non-trivial task. In some embodiments, correlate actions mod320individually determines a one-to-one correlation of problematic events to the corrective actions taken. Alternatively, correlate actions mod320determines a correlation based upon the frequency of any given problematic event occurring in the first system state with a frequently utilized corrective action that is used to remedy a pre-defined portion of the problematic events.

Processing finally proceeds to operation S275, where graph creation mod325creates a causal dependence graph based on the correlations made between the corrective actions taken to the set of computing events and the first sub-set of computing events included in the first grouping (discussed in connection with operation S270, above). In some embodiments of the present invention, the causal dependence graph is used to determine which corrective action (if any) was used most frequently on the first sub-set of computing events. In some embodiments, graph creation mod325uses an action list (AL) and a dictionary of action flows (such as action flows602shown inFIG.6).

In some embodiments, a computing event includes any IT operation. In some embodiments, a group of computing events (such as event grouping406shown inFIG.4) are created in order to efficiently process a set of tasks for an enterprise system; however, it is not necessarily the case that every grouping of events that is identified and/or created will yield a favorable result. In some embodiments, it is necessary to observe how these groups of computing events (herein referred to simply as “events” or “event groups”) alter the state of a computing system(s) in order to determine whether the event grouping is done correctly. It is important to note that the determination of whether the event groupings are done correctly based upon how the computing system(s) is altered is a non-trivial determination.

The benefits of utilizing embodiments of the present invention include: (i) observability data is precise; (ii) entity related information that are contained in logs are precise; (iii) in enterprise-related applications, there is a tendency for multiple faults to occur at the same time, and due to the temporal nature of these faults, these faults are sometimes incorrectly grouped together; (iv) logs, metrics, and/or traces footprints contain non-obvious clues about the actions taken to resolve an issue that corresponds to a particular event; and (v) the ability to leverage observability data (such as observability data414and420) to correlate actions and events is useful in determining related events, thereby building action driven fine granular event groups.

Some embodiments of the present invention learn action flows and their resolutions from logs using initial output of event grouping service and analyzing the logs from the period starting from when fault is first triggered to the time it got resolved. This process will help in learning action flows in order to resolve each event.

Some embodiments of the present invention build an action causal dependency graph (such as infer action causal dependency graph426referenced inFIG.4) by inferring causal relationships between frequently occurring action flows. This process provides the following advantages: (i) helps in noise reduction in action flows (that is, actions that are not necessarily required); (ii) observes the change of state of the computing system to identify if it resulted in resolving the issue in a given event; and (iii) associates the action with the state change and action verification leading to a state change from an unhealthy state to a healthy state (as shown in aspects ofFIG.5).

Some embodiments of the present invention provide a feedback mechanism that results in fine granular grouping of events based on actions that are taken to resolve certain events.

Some embodiments of the present invention provide a mechanism for learning action flows. This inputs for this mechanism include historical event groups and observability data. In some embodiments, historical event groups includes information that indicates a group of events, services and/or components that are impacted, and associated entities (such as pods and deployments). In some embodiments, observability data includes log data and metrics data.

Using these inputs, the mechanism operates as follows:

For each group (G) that is being analyzed, the following actions are taken:(1) During the period of time in which the group (G) was active, embodiments of the present invention use a pre-trained classifier to classify each log line into one of the classes (such as “action,” “error,” and/or “healthy”);(2) If the label type is “action,” then embodiments of the present invention identify they type of action from the action classes. In some embodiments, the possible action types are: restart pod, increase memory, decrease memory, scale up services, scale down services, de-duplicate pods, create additional instances, etc.; and(3) For each event (e) that is a part of the group (G), some embodiments extract tuples for event (e) using observability data. One example of this extraction includes the following: {“entity_1”:{“action_type”:“restart_pod”, “ts”:2382929}, “entity_2”:{“action_type”:“increase memory”, “ts”:7939292}}], where entity_1, entity_2 are entity names.

Additionally, some embodiments check logs to see whether there are any “golden signals” in order to detect a system status. As used throughout this document, the term “golden signals” includes, but is not necessarily limited to, graphs showing: computer latency, CPU saturation percentages, communications signal strength, memory utilization, etc.

Some embodiments check the state of the computing system with a timestamp plus a certain delta, with the delta being a function of the action type (referenced above). Some embodiments update a dictionary with the system state outcomes as a success or failure. One example of this update includes: {event_1={“entity_1”:{“action_type”:“restart_pod”, “ts”:2382929, “delta_ts”: 2382929 , system_state:“unhealthy”}, “entity_2”:{“action type”:“increase memory”, “ts”:7939292}, “delta_ts”: 7939352 , system_state:“healthy”}. Finally, some embodiments add this dictionary to an action list (AL).

The outputs for this mechanism includes the action list (AL) (that is, the list of all of the corrections that need to be made). For each event that is analyzed, embodiments of the present invention receive a sequence of actions, with each action being performed on an entity at time (t) and the resulting system state at a later time (t+t′).

Flow diagram400ofFIG.4shows a general system architecture for implementing embodiments of the present invention.

In some embodiments, the events that make up event grouping406includes computing events such as alerts, log anomalies, metrics-based alerts, and/or other metrics-related anomalies that are received from a set of disparate sources (such as a set of virtualized distributed computing servers).

In some embodiments, observability data414includes visual data (such as graphs) that show information relating to the performance of a given computing system when faults are resolved (through the operations of fault remediation module412). In some embodiments, observability data420includes visual data (such as graphs) that show information relating to the performance of a given computing system when these faults are active. In both instances of the observability data, this data includes memory workload, CPU workload, etc.

After receiving the observability data (both observability data414and420), embodiments of the present invention start to identify certain actions (such as actions taken by a user, including a systems engineer) with a set of computing events. This is done in order to provide helpful input data for learning useful action flows module422. With this data, learning useful action flows module422helps to build a causality dependence graph (such as infer action causal dependency graph426).

Flow diagram500ofFIG.5shows a timeline of events that occur in order to ultimately correlate a set of actions with a set of entities.

Flow diagram500includes: alert state502, alert state504, alert state506, set of entities (unhealthy state)508, set of actions510, action to entity correlation512, set of actions514, and set of entities (mixed state)516.

In some embodiments, the alert state (such as alert state502,504, and506) refers to an event state. These event states are the state of a computing system (or systems) when a pre-defined set of events are occurring on the system.

Flow diagram600ofFIG.6shows a series of specific action flows that can be filtered and clustered.

Flow diagram600includes: action flows (AF)602, frequent action sequence604, causal dependence graph606, action flow clusters608, action sequence610, and action sequence612.

Starting with an action flow (AF) (such as action flow602) and/or a dictionary of action flows, embodiments of the present invention generate an action causal dependence graph (such as graph606). Using association rule mining, embodiments of the present invention start with a series of action flows (such as action flow602) and extract a series of common action flows with their frequency (such as frequent action sequence604). In some embodiments, action flows are clustered based on an overlap of actions (as shown by action flow clusters608). This process ultimately results with each group of action flows being represented by a causal dependence graph (such as graph606).

Event grouping diagram700ofFIG.7is a graphical representation of a fine granular event grouping diagram. Some embodiments of the present invention project the action flows for each event in a group over the causal dependence graph in order to determine if there are any disjointed graphs (such as disjointed graph702shownFIG.7). This is ultimately used as feedback to the event grouping service. In some embodiments, if the projection returns disjointed subgraphs, then it is possible to conclude that the events are not correlated to each other. This is helpful feedback to the event grouping service that indicates that there may be two different faults that must be remediated by a support engineer. In some embodiments, the event grouping service (EGS) uses this feedback for finer groupings, especially when there are simultaneous multiple faults that are found.

Data communication: any sort of data communication scheme now known or to be developed in the future, including wireless communication, wired communication and communication routes that have wireless and wired portions; data communication is not necessarily limited to: (i) direct data communication; (ii) indirect data communication; and/or (iii) data communication where the format, packetization status, medium, encryption status and/or protocol remains constant over the entire course of the data communication.

Receive/provide/send/input/output/report: unless otherwise explicitly specified, these words should not be taken to imply: (i) any particular degree of directness with respect to the relationship between their objects and subjects; and/or (ii) absence of intermediate components, actions and/or things interposed between their objects and subjects.

Without substantial human intervention: a process that occurs automatically (often by operation of machine logic, such as software) with little or no human input; some examples that involve “no substantial human intervention” include: (i) computer is performing complex processing and a human switches the computer to an alternative power supply due to an outage of grid power so that processing continues uninterrupted; (ii) computer is about to perform resource intensive processing, and human confirms that the resource-intensive processing should indeed be undertaken (in this case, the process of confirmation, considered in isolation, is with substantial human intervention, but the resource intensive processing does not include any substantial human intervention, notwithstanding the simple yes-no style confirmation required to be made by a human); and (iii) using machine logic, a computer has made a weighty decision (for example, a decision to ground all airplanes in anticipation of bad weather), but, before implementing the weighty decision the computer must obtain simple yes-no style confirmation from a human source.

Automatically: without any human intervention.