Automate Problem Time and Solution Time with AIOps..

MTTR or Mean Time to Repair is one of the key performance metrics used in AIOps to measure the efficiency  of an application, infrastructure or workflow in terms of its maintainability, lifecycle cost and overall performance.  Taking the big picture of MTTR, it entails 2 parts: 1. The problem Time 2. Solution Time. Problem Time includes the time taken to detect the anomaly and know its root causes. Solution time includes the time required for resolving the identified anomalies and verifying return-to-normal state. The main aim of AIOps is to reduce the problem time which eventually results in reducing solution time. AIOps-powered APM solutions enable the IT teams to automate problem time and solution time thereby significantly reducing the overall impact of anomaly. This ultimately results in shortening the cost of enterprise time and resources especially in test and quality assurance environment. Automating problem and solution time of anomalies directly refers to the application of AI/ML  in detection (MTTD), root cause analysis (MTTK) and verification (MTTV) of anomalies. 
AIOps in Anomaly Detection-Reduce MTTD
Traditional monitoring and DevOps tools require hours to detect anomalies, demands IT teams to have domain specific knowledge regarding the workflow, infrastructure and application while  configuring anomalies, and sometimes fail to detect real problems among irrelevant alerts. With AIOps IT teams can easily trace anomalies that cannot be captured even through the latest preconfigured monitoring systems. AIOps helps to connect data from various sources like logs, traces etc to alerts, incidents and  events thereby simplifying the integration process. It can even correlate various events, incidents and alerts resulting in reduction of noises and also increases the probability of noticing the critical incidents. For detecting the anomalies, traditional monitoring and legacy tools requires live, streaming metrics to monitor the infrastructure and a manually created threshold for all those metrics to determine the normal state of various components like applications, containers, vms, databases, frameworks etc. AIOps completely eliminates this need by automatically setting up the baseline threshold for environment by monitoring individual applications. Here AI/ML uses time-based correlation and contextual correlation to avoid alert during normal workflow and to detect anomalies of paired metrics. AIOps will thus monitor the streaming metrics and thereby detects the anomalies in real time without any human intervention.
AIOps in Root-Cause-Analysis- Reduce MTTK
Once an anomaly is detected, the next step is to determine the root cause of anomaly and resolve it. Majority of anomalies require some sort of research and troubleshooting to recognize their root cause. Root cause analysis by legacy tools needs manual walkthrough of error logs to discover the timeline of first error.This leads to wrong results and fails to identify the real cause.
AIOps solve this issue by automatically creating anomaly timeline by real time monitoring of data streams, and identifies anomalies origin and state with the use of historical and contextual correlation. AIOps point outs key details that requires quick attention and streamlines information which in turn helps the IT teams to take immediate actions  as well as to prevent problems from escalating. IT teams will get deeper insights into the incidents, its potential impact and a broader understanding of the full context related to an incident. Use of AI/ML helps to classify the incidents, identify its probable causes and strike out the incident area and immediately isolate it with ease.Thus through AI/ML, AIOps reduces the time spent on analyzing the root cause i.e, MTTK by a considerable amount thereby increasing the overall performance of IT team.
AIOps in Verification- Reduce MTTV
AIOps also automate MTTV component of MTTR. An anomaly is resolved once it retains its normal state or enters a new normal state. AIOps verifies and make sure that the infrastructure retains its normal state by observing and monitoring the live ETL metrics from various sources. This helps to give rapid identification once the environment regains its normal state. Traditional tools require huge time, procedures and domain-specific knowledge to identify, acknowledge and solve incidents. With the use of AIOps, the incidents identified will be immediately direct towards the IT team responsible for resolving the issues. It can automatically take actions to remediate the incidents with little intervention of humans in no time. Correlation of events, prevention of recurring incidents etc are added benefits of using AIOps.Using AI/ML ensures continuous improvement through feedback as and when the model collects more data.
Thus AIOps enables faster identification and analysis of issues that in turn results in faster incident response and remediation which ultimately ensures lowering MTTD, MTTK,MTTV and overall MTTR. 
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