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Preventive Cloud, Application and Network Failure Monitoring based on Learned Observation

Observe, detect and monitor for abnormal, unusual and unknown instance of failures

Our Story

With continuously changing stream of data you need preventive based observation tool which automates summarization and pin-point and prevent blindsiding failures..

Our Vision

Simplify, optimize and automate detection of issues, delays in troubleshooting effort. as most of current effort are based on manual processes involving guess work and dependency on expert knowledge.

Technology

Preventive observation based learning helps in moving from static model based failure analysis to more dynamic based observations. choosing data reduction, patch-up data gaps, proper heuristic and machine learning techniques for faster preventive failure detecting and root cause analysis.

How it works

Automated Aggregation

Analyze huge stream of alerts, alarms, performance metrics, ticketing data from across hybrid environment of cloud and network platform, business application (Legacy and Microservices ) Use proper heuristics and machine learning techniques.

Handle TWO important hurdles to deal with -

Learn to Reduce information overload,

differentiate useful and not so useful data.

Finding the failures and root causes that matter.

Features

Process for information reduction.

Preventive check list generation - mitigate issues before impact

Look ahead alerts- predict impacts or future alerts.

Automated repeat instances of issues root causes analysis.

Observation based learning Observe, detect and monitor for abnormal, unusual and unknown instance of failures

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What are Observations: Observations are set of alerts, alarms and performance metrics along a time span which represent stream of data points to enrich, filter out, differentiate useful and not usefulness for learning, detect abnormality and further analyze for unusual and previously not seen issues, failures and root causes.

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Challenges:

Observing huge streams of data for blindsided failures.

Process to reduce information overload- differentiate useful and not so useful data.

Finding the failures and root causes that matter - critical service and business transaction, resource failures and more.

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Speed up troubleshooting effort -Minimize guess work and reduce expensive work arounds and over dependency on experts

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Predict repeat failures - detect earlier (before) actual failures, explore previously not seen or new or complex failure.

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Preventive failure analysis for Business application failure. Cloud platform failure. Infrastructure failure. Mobile network failure.

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Detail out legacy, cloud and on-premises systems and business application disruption issues(what, where and how) before actual disruption through Preventive checks and problem detection, root causes and (fully) deterministic and focused troubleshooting. Predict impact for audit and manual confirmation.

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Preventive check list generation- mitigate issues before impact. Look ahead alerts - predict impacts or future alerts. Automated (some) root cause analysis. Knowledge representation for complex troubleshooting.

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Use knowledge representation tool for easy and more deterministic troubleshooting effort- provides reasoning(figure out what happened).

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Alerts for CI/CD, Expert review and on-site verification and quick turnover.

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