causality event correlation using artificial intelligence · 2018-10-25 · causality event...
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Causality Event Correlation Using Artificial Intelligence
© 2018 ProphetStor Data Services, Inc. All Rights Reserved
1
Causality Event Correlation Using Artificial Intelligence
Causality Event Correlation Using Artificial Intelligence
Table of Contents
The future of IT Ops .......................................................................................................................... 2
Causality event correlation algorithms ............................................................................................... 3
Learn more ....................................................................................................................................... 3
© 2018 ProphetStor Data Services, Inc. All Rights Reserved
2
Causality Event Correlation Using Artificial Intelligence
The future of IT Ops
Artificial Intelligence for IT Operation (AIOps) is the next generation of technologies in IT management. As
Gartner defined in the Market Guide for AIOps Platform:
AIOps platforms are software systems that combine big data and AI or machine learning functionality to
enhance and partially replace a broad range of IT operations processes and tasks, including availability and
performance monitoring, event correlation and analysis, IT service management, and automation.
AIOps uses AI and machine learning technologies to turn copious amounts of data into meaningful insights
and foresights, allowing systems to function independently, reduce operational costs, and increase efficiency
and productivity. For example, AIOps has machine learning to detect unusual event sequences in the logs,
which are collected from IT devices. This feature can largely shorten the time IT administrators spend on
addressing possible issues on all of the logs.
AI is becoming omnipresent as industry leaders such as Apple, Microsoft, Google, and Amazon use AI for
their products and services. Traditional IT Ops cannot keep up with the dynamic landscape of IT operations
and business models.
Today's AIOps platforms go beyond traditional monitoring solutions like APM (Application Performance
Monitoring and Management). Traditional monitoring solutions may only show the performance metrics
collected, and a more advanced solution is needed to decrease the mean time to detect (MTTD) and the
mean time to resolution (MTTR).
An AIOps platform detects anomaly events, correlates the causality of events
between different layers, and shows the related path routes and the affected
entities and performances by the anomaly events. AIOps provides strategic
insights to resolve a problem and foresights to help execute proactive actions
to prevent repeated incidents. Prophestor's Federator.ai® takes AIOps a step
further
ProphetStor's AIOps software, Federator.ai®, offers advanced solutions that APM cannot provide. Among
other AI features, such as detecting and predicting hardware issues, Federator.ai® correlates causality events
using the different types of AI technologies to quickly track down root causes and effectively and efficiently
fix problems.
Federator.ai® is equipped with an AI engine called Data Correlation and Impact Prediction Engine (DCIE) to
build correlations between different entities of application, virtualization and physical layers. It uses graph
models to process the relationships among objects. The graph-based data structure and search algorithms
help Federator.ai® find the possible causality events of an application anomaly more effectively and
efficiently in a large-scale IT environment.
© 2018 ProphetStor Data Services, Inc. All Rights Reserved
3
Causality Event Correlation Using Artificial Intelligence
Federator.ai® cross-layer causality event correlation process.
Causality event correlation
algorithms
In the figure above, Federator.ai® correlates the
causality events of the underlying layers when an
anomaly is detected in the upper application
layer. In the left diagram (Step 1), a transaction
latency anomaly event is detected in the
application layer, which is raised from a database
system (DB). In this example, users need to find
the possible causes of this event.
Federator.ai® DCIE uses multiple algorithms
based on four types of event correlation analysis
to correlate causality events and diagnose the
root cause of the application anomalies:
Time-related: Events usually occur in the same
time frame or are based on a certain time-order
pattern.
Dependency-related: Events with the related
entities on the different layers usually occur
together.
Content-related: Events with similar contents
usually occur at some specific points of time.
KB-related: KB (Knowledge Base) may include
the indicators of the causality among events
and the root causes of the problem.
In Step 2 of the above figure, a CPU loading
anomaly is also detected in one of the ESXi hosts;
at this point, it is not known whether this ESXi
host is the cause for the transaction latency
anomaly in the DB.
Step 3 shows how DCIE correlates the anomaly
events in CPU loading and network received traffic
in the physical layer to the database event in the
application layer. Federator.ai® DCIE builds this
correlation by using causality event correlation
algorithms to determine if any combination of
events has a higher possibility of correlation
patterns than others from all of the events
detected in different layers.
Traditional IT solutions require the user to find all
anomalies without AIOps, a time-consuming
process. As the figure shows, IT administrators are
no longer left solely to solve anomaly events
detected by APM or reported by users.
Federator.ai® can help them narrow down the
problems to a few suspicious physical or virtual
nodes or devices.
Learn more
To learn more about ProphetStor AIOps solutions,
visit us at http://www.prophetstor.com/federator-
ai/