The Role of Machine Learning and AIOps in Network Performance Management

Companies ask IT teams to do more with less. This was the case far before COVID-19 foisted an overnight shift in business operations and financial instability upon the world. But as the global pandemic continues, organizations are struggling more than ever to manage competing network resources, growing user demands, complex troubleshooting challenges, new digital transformation initiatives and technologies, and more. IT directors are doubling their efforts to find effective ways to improve the network while reducing operational costs. Many often lean toward specialized toolsets or onboarding engineers with broader skillsets to accomplish these goals. But it’s painfully clear that in most cases, enterprises need to consider a much more holistic and foundational approach. The question is, what’s the best strategy?

AIOps is one approach to streamlining IT operations that has been gaining traction over the last few years. Let’s explore the role of machine learning (ML) and AIOps in modern network management and the many ways it can help IT administrators transform their networks to meet today's challenges.

Understanding AIOps

What is AIOps? According to Gartner, it “combines big data and machine learning to automate IT operations processes.” To put it another way, it’s the next generation of IT operations, enhanced by ML and artificial intelligence (AI).

True AIOps technology consists of three key components. The first is the ability to ingest a broad range of useful data across your IT environments. This includes data in motion and at rest and both real-time and historical insights from various sources (flow data, packet data, APIs, etc.). Next, it must conduct dynamic analysis across all those data sources using advanced ML to identify patterns and correlations. This allows the platform to contextualize big data, identify root causes and even provide predictive insights. And finally, AIOps technology can allow you to actively respond to issues as they arise. As the system learns patterns and becomes more intelligent, it should be able to either recommend or apply remedial actions via automation. Some solutions rely on pure statistical processing to improving IT operations, but AIOps technology takes a more sophisticated approach with these three components.

Transforming IT operations

AIOps provides the intelligence required to establish an accurate baseline for the network from a multi-dimensional perspective. How many users do you need to accommodate? In which locations do they typically operate? Which applications and services require the most bandwidth, and at what times? Automated management and monitoring across these types of critical insights provide your team with better visibility into any potential abnormalities. This allows you to become much more agile and proactive when it comes to solving network issues before they impact user experiences and the bottom line. It also enables you to identify and remove network resource waste and inefficiency.

Using AIOps, you can apply advanced, ML- and AI-based analytics to automate a wide range of tasks your IT team would typically manage. This includes everything from ongoing monitoring to in-depth troubleshooting processes. The end result is a level of automation that reduces skillset and training requirements for your current and future team members and enables them to spend time on other business-critical tasks.

Networking tool sprawl is another major challenge AIOps technology can address for IT teams. According to the last EMA Network Management Megatrends survey, more than half of network operations teams rely on between four and ten tools. These IT tools are often specialized to examine specific data sources and handle a precise set of issues. For instance, application performance monitoring (APM) solutions typically won't help solve network degradation anomalies, and IT infrastructure management (ITIM) tools are no good when it comes to fixing application downtime. AIOps can help reduce IT tool sprawl by ingesting disparate data sources and correlating insights to provide a level of visibility that would otherwise require multiple tools and solutions. This can mitigate the productivity challenges IT teams experience when toggling across a handful of networking tools each day (while reducing the need for unnecessary licensing costs, instructional requirements, etc.)

There is a broad range of additional benefits as well. As many organizations continue the rapid shift to cloud services, AIOps can provide in-depth network visibility that significantly reduces the operational risks of cloud migrations. The added agility and flexibility can free up time and resources that your IT team can direct toward planning and executing new digital transformation initiatives that better support the business. Additionally, AIOps technology can support more effective DevOps initiatives and adoption with more advanced network visibility and insights. In short, on top of the many direct upsides of AIOps technology, it can also drive and support other IT initiatives as well.

The road ahead

Relatively speaking, AIOPs is an early-stage technology that some organizations are still hesitant to pursue. But one thing is for sure: IT departments are in desperate need of modernization and tangible paths toward minimizing time and resource constraints. AIOps holds the key to a more automated, streamlined, and optimized approach to IT management that can help your team more quickly and effectively identify and resolve network issues.

Those unsure about its role in the future of network performance management and IT operations should consider how quickly ML and AI use cases have transformed other sectors such as the healthcare and financial services industries. With that in mind, it’s a safe bet that AIOps will be one of the most revolutionary technologies over the next several years.

Clark Zoeller is Technical Product Manager at LiveAction.