In this special guest feature, Girish Muckai, Chief Sales & Marketing Officer, HEAL Software Inc., discusses four critical capabilities to look for in AIOps tools. Every AIOps tool brings something unique to the table. Evaluating your options based on these four features can help ensure you are set up to move from a break-and-fix to a predict-and-prevent model. Heal Software Inc., the innovator of the game-changing preventive healing software for enterprises known as HEAL, fixes problems before they happen.
2020 has been a year of realizations for business leaders around the world. Most significantly, business leaders have seen the need to scale down operational costs and introduce automation in data centers. Businesses have had to increase their focus on maximizing uptime and optimizing customer experience, all while operating with skeletal staff and seeing an exponential increase in online footfalls. Artificial intelligence for IT operations (AIOps) tools have proven to be the way forward for enterprises to achieve these objectives.
AIOps tools provide observability across disparate silos (cloud, on-premise or hybrid environments), employ artificial intelligence and machine learning (AI/ML) techniques to proactively detect anomalies, and event correlation to expedite root cause analysis and reduce mean time to resolve (MTTR). They also derive insights and business intelligence on top of the huge volumes of telemetry and transaction tracing data that they capture. However, these tools are not all created equally, below are some critical capabilities that tools need to possess to progress beyond the traditional role that IT operations teams are expected to perform and help them achieve the objective of a zero-downtime enterprise.
Graduating from Incident Resolution to Preventive Healing
Many AIOps tools use AI/ML techniques to detect issues; however, remediation is after the fact leveraging automated orchestrated workflows through IT service management (ITSM) integrations. Hence, they provide incident resolution after the fact and focus on MTTR as a measure of their efficacy. However, with new preventive healing systems it is possible to predict issues before they occur via patented techniques like workload-behavior correlation. This essentially considers the effect that a certain workload signature has on underlying system resources and flags those transaction patterns which are likely to cause an issue in the foreseeable future. Once an issue is detected, there are techniques in place to avert it, which could include dynamic optimization or “shaping” of workload so the underlying system behavior remains unaffected or dynamically provisioning additional resources in cloud environments so the system can handle workload surges. By generating a lead signal on a potential issue before it even occurs and putting in place techniques to avert it –– the enterprise can focus on “number of outages averted” as a measure of efficiency and move toward zero downtime.
Deriving Insights to Aid Operations Teams
Most AIOps and application performance management (APM) tools are turning to observability – the newest monitoring tool. The latest report by Gartner, “Innovation Insight for Observability,” looks at how organizations are using telemetry data captured from various sources to enable DevOps and site reliability engineering (SRE) teams to reduce application downtime, minimize incident resolution effort and improve overall customer experience. When used correctly, this data can aid and accelerate root cause analysis. Contextual data captured by the AIOps tool at the time of the incident should contain enough diagnostic data on the state of the system to allow IT operations analysts to establish the chain of causation, accurately pinpoint the origin of the failure and take steps to address it. Historically it would require logs, forensics, query-level statistics from the database, code-level instrumentation and drift tracking to acquire this level of data. Leveraging modern AIOps can help teams significantly reduce MTTR on the issues that cannot be predicted in advance (like hardware glitches, network and storage outages or unavailability of third-party dependencies like APIs and payment gateways).
Protection of Existing Tool Investments via Integrations
More than a single AIOps tool is required to provide visibility across disparate silos in today’s increasingly complex hybrid digital environments. It is paramount that any new tool provides integrations with existing APMs to capture required telemetry data to learn application behavior and generate insights, a gamut of ITSM tools so automation of ticketing workflows can be achieved and visualization and notification tools like Slack or JIRA to foster collaboration among the troubleshooting teams. This integration enables the enterprise to incorporate newer, more powerful features in its operational setup while protecting existing tool investments and sparing the teams the pain of onboarding new tools at the expense of safeguarding the existing ones.
Some integrations, including those provided by ITSM platforms like ServiceNow, connect the AIOps tool to business processes outside IT, including HR, DevOps, SecOps, risk and governance. Others focus on providing integrations with container management solutions like Docker and Kubernetes so DevOps and Agile methodologies can be implemented across the enterprise for continuous deployment, maintenance and management of applications.
Forecasting Capacity for Intelligent Scaling
AIOps tools sit on top of a plethora of historical data captured from various sources; this data lake is invaluable when it comes to generating insights and planning for future scaling. However, simply examining the growth trends on system resources is not sufficient for forecasting capacity. It is also imperative to note any significant internal or external factors which could result in a surge of workload. Take for example the acquisition of a bank by a financial institution, a big sale for an e-commerce provider or a marketing event which is likely to result in a sudden increase in traffic on a website. More intelligent scaling recommendations can be achieved by factoring in workload growth trends and analyzing system hotspots in conjunction. The objectives of such an exercise are to project workload growth and identify capacity choke points, as well as to compute business aligned capacity forecasts with a what-if analysis. This helps identify incorrectly provisioned resources on the cloud to cut back on infrastructure budgets or scale up resources as the need may arise.
Every AIOps tool brings something unique to the table. Evaluating your options based on these four features can help ensure you are set up to move from a break-and-fix to a predict-and-prevent model. Business leaders need to evaluate the gaps that current AIOps offerings suffer from as they apply to the mitigation of downtime, reduction of cost and effort in issue resolution and business-aligned growth planning to manage costs in a multi-cloud environment. Moving toward preventive healing is the only way forward in these tumultuous times to ensure your data center is up and running 24×7 and your applications are always available.
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