AIOps (Artificial Intelligence for IT Operations) – An Explanation

AIOps is a relatively new industry term that is increasingly being associated with many different companies.  No AIOps company started that way – they have all evolved from Network monitoring, Application monitoring, Service Desk or Infrastructure monitoring. This new space is described by Gartner as having two main components at its heart: big data and machine learning. These are surrounded by monitoring, service desk and automation to give continuous insight into the performance of the IT systems and to provide dashboards and reports to the business. The premise of AIOps is to give management a view into how IT is supporting the business.

The management products in this field overlap but don’t necessarily conflict with each other. They should be deployed together, and integrated, to give both visibility and control to the user, application and IT infrastructure, to enable automation.

AIOps IT ManagementSo where are we today?  Most large enterprises are typically using dozens of management and monitoring tools – a combination of Application Performance Monitoring, Network Performance Monitoring and an abundance of silo-specific Infrastructure Monitoring tools.  For the most part, the service management and change management tools work well but the monitoring side is a major problem as the tools don’t relate to each other and essentially speak different languages.  Some organizations have implemented dedicated AIOps tools that simply collect and analyze alerts or analyze logs from all these other products.  These alert and log aggregation tools are helpful with troubleshooting, but they are all “post-facto” solutions that can’t be used for real-time performance monitoring or for proactive problem prevention.  Most monitoring tools are also silo-specific, are not integrated, offer no cross-silo correlation, and lack any understanding or context of the applications.  This means they are primarily reactive-only tools.  They get you to the scene of the accident faster, but they don’t prevent the accident from occurring!

In order to really benefit from AIOps a new, non-siloed and application centric approach is needed. Rather than looking at the user, application, and infrastructure, independently the AIOps platform should follow the journey of the application from end user into the IT infrastructure and back out again. The business is not interested in what technology is deployed (the move to cloud providers proves this) what they want to know is if one or more of the applications that are running their business are performing as they should. There are many recent horror stories of critical applications becoming unavailable and hurting the business and its reputation.

Gartner AIOps DiagramArtificial intelligence for IT operations (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.” (Gartner – “Market Guide for AIOps Platforms” – Will Cappelli, Colin Fletcher, Pankaj Prasad. Published: 3 August 2017)

So where does VirtualWisdom from Virtual Instruments fit into this space?  VirtualWisdom is a software platform that gathers big data from its deployed software and hardware probes, collecting and analyzing wire data from all infrastructure devices, at line speed, together with machine data from the server, switch fabric, and storage components. This information is correlated into a ‘single pane of glass’ view.  VirtualWisdom probes can process more data in an hour than Twitter generates in a week – so handling the scale and throughput needs of larger enterprises are a core product capability. This data is analyzed by the platform to give an easy to understand interface into how the applications running the business are performing. The combination of machine-learning and deep experience enable us to provide concrete advise on changes that should be made to improve performance or prevent issues before they become a problem.

The key elements to AIOps are managed by VirtualWisdom to give you:

  • Automation: Using experience to recommend how your IT infrastructure can be optimized VirtualWisdom with suggest courses of action to assure and improve application performance.
  • AI-based machine-learning: Understanding normal patterns of application activity to ensure you are only alerted when any element, used by the application, trends outside its normal activity. It will also advise on what course of action to take.
  • Visualization: Providing a simple to understand view of the application and infrastructure that can be customized to any managers requirements – from a simple red, yellow, green management dashboard to a full deep dive of components, IO metrics and capacity.
  • Correlation: Bringing together IT infrastructure wire and machine data with service desk and application performance monitors to give a single, simple to understand live report.
  • Auto Discovery: Automatically identifying the infrastructure devices installed and mapping the topology of the IT Infrastructure to enable a single view of elements the applications are using, where resources are shared and where there is contention.
  • Data ingestion: Data is ingested from the Application performance monitors, service management tools, and all elements within the IT Infrastructure in real time. No other platform can come near VirtualWisdom in both capacity and granularity of data processed to give a true validation of how the applications and its supporting infrastructure are performing.

True AIOps is a real-time, end-to-end, view of how the critical applications running a business are performing and how the ever-growing IT infrastructure supporting them can be optimized.  VirtualWisdom is the leading AIOps platform chosen by the largest enterprises worldwide to manage their critical application and the underlying IT infrastructure performance.

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