IT organizations are improving network management capabilities through the integration of artificial intelligence (AI) and machine learning (ML). A recent report by Enterprise Management Associates, AI-Driven Networks: Leveling Up Network Management, sheds light on this approach of utilizing AI/ML in IT operations solutions, commonly known as AIOps.
AIOps combines big data and machine learning techniques to support IT operations functions. Its primary aim is to improve root cause analysis, enable predictive insights, and automate responses, all while significantly reducing mean-time-to-resolution (MTTR) and elevating the digital experience.
EMA asserts that confidence in AIOps remains high, with nearly 92% of organizations believing AI/ML-driven network management can lead to better business outcomes. In fact, 40% of organizations have already integrated AI/ML technology into nearly all aspects of their network management processes.
Drivers of AIOps adoption
The top priority for using AI/ML is network optimization. Organizations are looking for ways they can tune the network to best meet specific business needs. What’s worth noting is that IT executives are increasingly placing their faith in AI/ML techniques to facilitate this critical endeavor. Additionally, other important use cases for larger organizations include automated troubleshooting, intelligent alerting and escalations, and predictive capacity management.
Top benefits of AIOps-driven networks
Most organizations apply AI/ML and AIOps to network management via their network management and network infrastructure solutions. Although domain-agnostic AIOps products such as Moogsoft and Big Panda exist, they are somewhat less prevalent in network management use cases.
AIOps offers significant advancements to monitoring the network. The biggest opportunity is network optimization. The network operates at its best when AI/ML identifies and correlates events in real-time, resulting in a smoother overall system. The report also indicates benefits in network agility, security, and resiliency.
Riverbed NetIM adds AI/ML techniques to improve results
With the addition of dynamic thresholding in Riverbed NetIM infrastructure monitoring, all Network Observability products support AI/ML techniques. NetIM now uses dynamic baselining that automatically and continuously updates historical performance baselines to identify significant changes in behavior. Instead of setting and tuning per device static thresholds for utilization, memory, and CPU, Riverbed NetIM dynamically baselines these metrics to identify significant changes in behavior. As a result, it significantly reduces “noise” stemming from non-actionable alerts and minimizes ongoing maintenance related to manual threshold tuning.
For a deeper dive into Riverbed NetIM IT infrastructure monitoring, click here. To explore the myriad of benefits and applications of AIOps, download our ebook today.