The digital transformation has both simplified and added complexity to the management of modern IT infrastructure. IT environments are increasingly becoming hybrid, complex, and fast-moving. With containers, microservices and multi-cloud adaption, the number of monitoring metrics that need to be tracked increases exponentially.
Today’s emerging and evolving technologies create a tsunami of operations data which is extremely difficult to track and decipher manually.
According to Gartner (2019), IT infrastructure and applications generate two to three times more data volumes every year.
It has become humanly impossible to manage the volume, variety, and velocity of operations data by ITOps teams. The monitoring tool sprawl has made things even more difficult to get appropriate data and find meaning insight and conclusions. Moreover, event noise suppresses real issues and causes increase in MTTR and missed SLAs. Issues get unnoticed till user calls about problems which becomes an embarrassing situation for IT teams. Buried with day-to-day operations, IT teams do not have time to take on new innovative initiatives.
What is AIOPS ?
AIOPS is simply artificial intelligence (AI) for IT operations, term originally coined by Gartner.According to Gartner, AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.The definition continues to expand as new tools arrive and new areas of IT operations come into play.
AIOps combines machine learning, analytics, and other technologies to improve and enhance IT operations. While these technologies have existed separately for years, AIOps brings them together for the first time to analyze IT processes and derive meaning insights from large datasets without human input.
AIOps, is helping enterprises identify in advance potential outages and performance issues before they negatively impact operations. Moreover, AIOps systems not just identify issues, or predict issues before they happen, but they react to events with intelligent, automated mitigation and remediation.
Here are few use cases in IT Operations:
Event noise reduction
To manage complex, dynamic infrastructure environments, IT teams use many tools and set corresponding alerts. With massive volumes of events, critical events are missed.
With an AIOps, machine learning to historical and real-time data to identify patterns and suppress events could be applied effectively. This enables reductions in event noise, while better ensuring the most critical alarms are addressed most quickly and effectively.
Predictive alerting
IT team scramble to take appropriate actions due to flooding of events. They typically fix the problem after event occurs which is too late in the game. Not only service levels and SLAs suffer but organizations end up paying a heavy price for failures of critical systems.
AIOPS provides the ability to apply advanced analytics to historical and real-time performance metrics. This further helps in establishing dynamic baselines, identifying anomalies and generate predictive alerts.
Predictive alerts avoid major system failures in advance. With advance warnings enable IT Operations team to take proactive action to remediate underlying issue.
Root cause identification
IT team struggle to find the real culprit in when multiple events are generated by various tools. It becomes humanly impossible to correlate these events to find the root cause of the problem. Many a times these failures ends up in enormous time consuming effort after failure happens. AIOps platforms help IT operations teams to find the root cause by advanced correlation and log and event analytics. AIOPS with machine learning can decipher through millions of monitoring data points, metrics, events, log anomalies, correlate and identify the root cause.
Automated remediation
IT teams act upon remediation of the root cause after experiencing the system failures.
Teams further have to rely on cumbersome operations manuals to find appropriate solutions.
Many a times solutions to these problems are not documented. Multiple errors crop up due to manual efforts especially from junior team members.
AIOps platforms not only help in finding the root cause but act on automated actions.
Auto-fulfilment of incidents helps in freeing support staff to devote time to higher value tasks.For complex issue resolution where automated action is not advised, AIOps with machine learning capabilities recommends due course of action to the operations staff for solving critical issues.
Summary
With increased complexity of heterogeneous infrastructure, hybrid clouds, voluminous data, pressure builds for IT teams to deliver business service value with minimal downtime. AIOps is emerging as both a cutting-edge discipline and next-level advantage for faster, more efficient infrastructure monitoring and management. Companies and IT leaders are starting to recognise the value. In a recent report, IDC analysts predict that, by 2021, 70% of CIOs will aggressively apply AIOps to cut costs, improve IT agility, and accelerate innovation.
Realizing these benefits, IT teams have no choice but to turn to AIOps technologies and approaches.
Prashant heads global delivery of Vyom Labs consisting of various practice units such as BMC and ServiceRize. Prashant has illustrious career over thirty years in information technology and has served at senior executive levels with global companies such as: IBM, BMC, Veritas and Citibank. Prashant has built, scaled and managed various IT, professional services & product development/support organizations. His technical expertise is in IT infrastructure management, automation and managed services. He is passionate about business transformation using cognitive technologies such as AI and machine learning.
