Harnessing the power of AIOps: Paving the way to autonomous networking
Artificial intelligence (AI) technologies like machine learning, virtual assistants, and process automation are driving revolutionary capabilities across the enterprise. Yet, arguably the greatest area of untapped opportunity is with AI and IT operations (a.k.a. AIOps).
What is AIOps?
AIOps applies AI and analytics to network visibility and management capabilities. AI in the network helps IT teams better understand what’s going on, optimize bandwidth efficiency, and act in response to changes in network behavior, context, and load. Essentially, these technologies can act as a virtual network engineer, multiplying your workforce with a 24 hour assistant. Moreover, AIOps serves as the foundation for creating a fully autonomous network. Much like self-driving vehicles, the tools we need to create an autonomous network are available today–it’s only a matter of putting them into practice and perfecting them.
The benefits of AIOps are enormous. According to Gartner, “organizations that automate 70+% of their network change activities will reduce outages by at least 50% and deliver services 50% faster.”
Autonomous networking: Step one is automating network understanding
For two reasons, today’s IT teams aren’t fit for comprehensive network analysis. First, under the pressures of trying to do more with less, IT teams aren’t growing as the pace of technological change accelerates. In a new study from Dynatrace, 76% of CIOs agreed it could become impossible to manage digital performance as IT complexity increases. Eighty one percent believe AI will be critical as a result, with 83% either already or planning to deploy AI in the next 12 months. AIOps is needed for IT to operate at the speed of today’s digitally driven, constantly changing business needs.
Second, machine learning and behavioral analysis are simply better at the job. AIOps virtual assistants are designed to help IT teams build a full, comprehensive view of network activity. They deeply understand what constitutes “normal” activity, for example, and can sift out potential performance- or security-affecting events. Furthermore, they go beyond identifying “normal” network behavior to maintain a dynamic picture of what constitutes this behavior as the network changes over time.
Specifically, AIOps virtual assistants provide:
- Intelligent analysis via sophisticated pattern recognition algorithms
- Predictive anomaly detection with actionable insights
- Expandable use cases for proactive network, application, and security optimization
As a result, teams gain a more thorough grasp of their IT environment, empowering them to design and maintain the playbooks AIOps tools need to start reacting to performance problems and service failures. Ultimately, this allows IT to spend less time on administrative management and more time on strategic work.
Autonomous networking: Step two is intelligently recommending and acting
Just as virtual assistants support contact center agents in automating post-call work and advising on which step to take next in the call flow, network virtual assistants provide expert guidance on design and problem solving, helping IT teams automate mundane management tasks. Specifically, AIOps virtual assistants provide recommendations to optimize multi-cloud environments:
- Which path an application should take, based upon performance
- When and where to add bandwidth, including cloud vendors
- Network changes and configuration settings to optimize application performance, based on business needs and service priorities — See how Masergy AIOps works
Learning from IT team feedback and using automation tools, AIOps can then be coached and trained to solve problems and modify the network for future situations as needed. Learn more in the guide to AIOps by Nemertes Research.
Paving the way: Deliver what AIOps needs
Network readiness is a large factor in AIOps success, as these tools must have the raw data, network visibility, and computing power they need to evaluate the IT environment. And in order to act, they need access to network bandwidth controls and configuration consoles. Here’s a checklist of things you’ll need for success:
- Data: AIOps should be able to see, ingest, and use configuration data as well as performance monitoring feeds from all the underlay components of the network. It should also be able to take feeds from other management tools, like software-defined network and SD-WAN controllers. The complexity of the network architecture, as well as the management environment will ultimately affect the use of AIOps. Agile, software-defined environments will best serve AIOps.
- Power to process: To make use of these data streams, AIOps tools need strong analytical capacity behind them. Whether provided in data center resource pools or in a service provider or cloud infrastructure, AIOps needs computer power to drive event correlation, real-time altering, and response.
- Levers and strings: There needs to be a software-defined network or SD-WAN controller through which it can drive changes in configuration. Where AIOps is not built directly into the core network management, it must have a means to act. In these cases, it needs comprehensive APIs for the network underlay as well as any overlaid apps and services are needed. By comparison, these solutions are more complex.
Now that you have a list of the necessary requirements, here’s an infographic with the four steps needed to pave the way for autonomous networking with AIOps.
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