By Nitin Kumar & Cuneyt Buyukbezci, Appnomic

According to Gartner, legacy monitoring tools still have approx. 30% of market share, this is a lot of investment for reactive measures, lack of full visibility with no preventive and remedial measures. Migration from APM and traditional monitoring tools to self-healing creates EBITDA improvement by rationalizing tools and headcount, automating operational processes, and reducing hardware costs. In addition, self-healing ensures business continuity (outage elimination) through early warning preventive healing. Migration to self-healing also improves productivity by cutting down false positive alerts, and resource time invested in troubleshooting. Such dramatic impact is due to value shift in five operational levers.

Migrate from MTTR to MTTP/MTTI

Business risk, operational cost and productivity impact of repairing problems is always higher than preventing them. Preventive self-healing leverages artificial intelligence to shift metrics from MTTR (Mean Time to Repair) to MTTP (Mean Time to Prevent) and MTTI (Mean Time to Identify) eliminating cost and risk attributed to system failures.

Eliminate alerts

Alert storms make it hard to identify the key insights towards IT operational stability, the issue is more profound if quality and completeness of data is lacking. Too many legacy tools, in too many different parts of the enterprise are detrimental to analyzing root cause, create operational inefficiency and higher downstream costs. Preventing issues before they happen will generate less noise and more efficiency.

Understand visibility gaps and address them

An average of eight to ten tool are deployed to monitor parts of the IT stack and yet a holistic picture is elusive. Visibility gaps cause undetected issues making it difficult to triage the problems and eliminating root causes. Self-healing observes the behavior of the users and the entire technology landscape (applications to infrastructure) creating a full picture.

Autonomous Prevention and Optimization

IT has been equipped with automation tools to configure systems and shift workloads. However, decisions and triggers still rely on human judgement and intervention. Self-healing can autonomously detect issues before they happen, decide the right triggers and actions leading to autonomous elimination of problems.

Right size capacity

Due to lack of visibility into workloads and correlation to system behavior, companies overprovision hardware to accommodate the largest incoming workload. Self-Healing provides Complete visibility and correlation between applications, systems and workloads creating an opportunity to write size hardware and thereby reduce spend.

For many enterprises that are stuck with the legacy monitoring and APM tools self-healing offers EBITDA improvement opportunity by changing paradigm in operations by improving service levels while eliminating cost and complexity.

By Nitin Kumar, CMC, CMAA

Keywords: AI, Cloud, Data Center

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