Evalution guide for AIOps Tools in your Enterprise

At the end of the day, all such AlOps tools measure their efficacy in terms of MTTR – Mean Time to Resolve an issue. This still implies that the issue has occurred and has been mitigated/resolved after the fact, and there has been a downtime, however small. Given the fact that enterprises have woken up to the tremendous business impact that even a few minutes’ downtime can have on customer experience, revenue and cost of resolution, organizations adopted a new buzzword in 2020 – negative MTTR. This essentially means resolving an issue before it even occurs.

With Preventive Healing solutions, it is. These solutions measure a metric that most AlOps tools do not – the effect of workload on system behavior. This implies that when application workload is not following trends of seasonality (time of day, day of the month, month of the year) or is anomalous with respect to number of requests inbound at a service, there is a cause for concern in terms of the effect it is bound to have on service behavior as well. If models are built to measure and learn this workload-behavior correlation, it is possible to warn ahead of time on an impending issue in an application.

The following section talks about important questions decision-makers in an enterprise need to address, to decide if they are ready to transition to preventive healing.

Ensuring high availability is now critical to business success. However, keeping your business continuously available implies dedicating valuable time and resources to the demanding task of keeping your IT infrastructure up and running 24/7/365. By moving to a preventive healing solution, an enterprise can be equipped to start moving towards a zero-downtime/negative MTTR issue resolution paradigm. This means that an imminent issue can be flagged and automatically remediated using various techniques, some of which include:

• Dynamically optimizing workload on the fly through tools like Cisco UVOM to reduce the load on the underlying infrastructure,

• Dynamically optimizing infrastructure in a cloud/microservice/containerized set-up using tools like Istio,

• Initiating service-centric mechanisms to heal based in time-synchronized contextual data; examples include forcefully terminating a non-essential database query holding onto session locks and preventing subsequent queries from being executed.

These healing mechanisms can be integrated with the underlying ITSM’s orchestration workflows seamlessly through REST interfaces. These empower the enterprise to gradually move from minimal to zero downtime, thereby reducing the costs of running ITOM, maximizing customer delight and helping keep operations centers lean.

As stated above, when it comes to AlOps tools, leaders would do well to focus on quality rather than quantity. The focus should be on adopting a toolset tailored to the demands of their enterprise – which could include one or all among these: application performance monitoring, infrastructure management, proactive alerting, event correlation, network monitoring and transaction tracing.

If there is a tool catering to a specific requirement not part of this feature set, it is imperative that prior to onboarding it, decision makers should check the number of Integrations that it provides out-of-the-box. Most tools operate in their own distinct space and come with their own native agents for data collection, or use data ingested by other APM/AlOps tools via REST APIs. They also provide API-based integrations with ITSM systems (like ServiceNow) and visualization/notification mechanisms (like Slack or Grafana). These integrations are Important to protect existing organizational investments In the ITOM setup and still make the best of what these niche tools have to offer.

Root Cause Analysis enables ITOM teams to pinpoint the cause of any incident and address it given all information at hand. An AlOps solution which provides contextual, timely, relevant, and accurate information on the state of the application in a concise, intuitive fashion so teams can perform event correlation and analysis effectively, is the need of the hour. In ITOM parlance, such dashboards with all service information and event correlations presented in a unified view are called a “single pane of glass.”

Service data pertinent to an issue which is extracted at the time of an anomalous event can have multiple dimensions. all of which need to be analyzed before root cause can be arrived at. Some examples of this data include logs, code traces, query level database statistics, configuration changes and diagnostic data called forensics, which tells us more about the state of the service at the time of the incident.

HEAL is the industry’s first preventive healing software for IT that can help IT problems before they happen. By applying a patented ML technique called workload-behavior correlation, HEAL can highlight anomalies in system behavior as a function of the current workload, called early warning signals. Such an advanced alerting mechanism, coupled with contextual data on the state of the system at the time of the anomaly, allows you the time to put in place the required measures to prevent the issue.

• Complete preventive solution for enterprise

• Autonomous healing to rectify issues, and enable ticketless monitoring

• Entire operating context of user journey, application and system in a single pane of glass

• Situational awareness that can precisely pinpoint root causes