As IT infrastructure grows more complex, NetOps teams should identify ways to ensure their networks remain operational and perform at peak levels. One option is AIOps.
Network operations, or NetOps, teams have traditionally used performance monitoring tools to manage the health and performance of corporate networks. However, the growth in network usage combined with disaggregated network deployments has led many to seek alternative performance monitoring methods, including the use of AI for IT operations, or AIOps.
This article compares traditional performance monitoring methods found in NetOps practices and discusses why teams will ultimately turn to AIOps adoption.
What is NetOps?
NetOps was derived from DevOps -- a set of IT software development principals that rely on operational feedback to propel the rapid development and implementation of applications. The goal of DevOps is to deliver more useful, reliable and secure digital services to end users.
NetOps principles adopt the Agile nature of DevOps and apply it to management, growth and scalability processes within network management practices. Using this methodology, NetOps tools automatically collect network health data. NetOps staff can analyze the data to make informed decisions on how to adjust network components and achieve the best network performance possible.
NetOps relies heavily on the use of automation to streamline network management and troubleshooting tasks. These automations speed up the time it takes to identify areas of the network that could be modified to increase performance in critical areas. NetOps administrators manually analyze this collected and organized data to make informed decisions.
The next step in NetOps: AI
While NetOps principles accentuate advancements in network performance agility, the expansion into public and private clouds and edge computing has increased overall complexities. This growing complexity can create bottlenecks during the network analysis phase of the NetOps process.
AI can analyze network health data, using tools with built-in data analysis functionality.
Until recently, the only option IT decision-makers had to eliminate this bottleneck was to bolster NetOps team staffing levels. With increased numbers, more people could view network analysis data that would be translated into actionable tasks. Thus, the ability to actualize network performance improvements is often offset by significantly higher costs required to implement said improvements.
To regain a balance between money spent and achievable performance gains, a second option has emerged in the form of AIOps. AI can analyze network health data, using tools with built-in data analysis functionality. It can deliver detailed and granular advice on what network changes teams should make to improve performance of the entire network or for identified business-critical apps.
NetOps staff will seek AIOps tools or risk falling behind
As more NetOps teams understand that the cost-benefit shortfall of throwing more human administrative resources at network health analysis is a losing proposition, they'll begin pushing for AIOps tools that will significantly reduce the data analysis phase of the process.
Most NetOps practitioners realize that as more apps, services and devices are added to the network -- and as networks expand further into various edges and clouds -- the amount of collected health and performance data will soon become so large it will overwhelm their teams. Thus, the ability to rely on AIOps tools that can automatically analyze data and deliver fixes for network performance issues is something that will become highly desirable.
