As the forerunner of AIOps, ITOA focuses on collecting and
unifying data for analyzing historical data across domains and solving problems
with observational data. However, ITOA solutions and domain-centric tools are
no longer sufficient to provide IT operations with deep insights into their
distributed multi-vendor, multi-domain and multi-technology IT infrastructure
in order to achieve the demanding business goals. AIOps is a software system
that blocs big data and AI as well as ML to improve and partially replace a inclusive
range of IT operational processes and tasks, including availability and
performance monitoring, event correlation and breakdown, IT service management
and automation . AIOps uses big data and machine learning techniques, to
provide proactive and predictive insights into problems and recommend - automated
- corrective measures. AIOps helps companies proactively plan and identify
business-related problems before they arise.
Function
blocks of an AIOps architecture
AIOps enables IT operations to overcome traditional IT
operations analytics (ITOA) strategies, abandon old, reactive processes and
react proactively by predicting problems and preventing outages. An AIOps
platform helps companies improve their IT operations by understanding their
application and IT infrastructure resources.
To do this, however, the development of a clear AIOps
reference architecture and strategy that uses the best tools is a must. The
tools integrated into the reference architecture form the key components of
AIOps for the provision of AI-controlled operations. An AIOps solution
comprises the following functional blocks:
Open data
ingestion
An AIOps platform collects all kinds of data from various
sources. This can include operational insights such as errors, logs,
performance metrics, log warnings, tickets, and more. The ability to collect
data from a wide variety of data sources is critical because it enables an
accurate, real-time view of all moving parts in hybrid IT environments.
Auto
discovery
Given the very dynamic nature of modern IT environments,
companies need an auto-discovery process that automatically collects data athwart
all infrastructure and application domains. Auto-discovery also categorizes all
infrastructure devices, the in succession applications and the resulting
business transactions. AIOps is able to do this because it knows how to
communicate with the various infrastructure and application units. Data is
collected from all types of entities - switches, routers, load balancers,
firewalls, Marketing Sponsored write for us , storage devices, hypervisors, virtual machines, application
entities, and more - whether physical, virtual, or logical. The automatic
detection of AIOps then feeds the discovered entities and relationships into
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