A Vice President of Network Operations at a leading IT firm had never encountered a technology solution with the capacity to correlate insights across multiple monitoring tools simultaneously. It was always one event at a time, subjected to manual processing. He felt there are shortcomings in today’s network tools and processes, especially within the complex and sprawling management domain of network operations. Consider the data center switching fabric, the wide area network (WAN), corporate local area networks, and the public cloud. Even network domains have more subdomains that add to their complexity. For instance, the LAN (Local Area Network) consists a Wi-Fi access layer and a wired Ethernet network. Consequently, the network leader would often struggle to correlate events across these two subdomains because he was using different tools to monitor them. Same with WANs (Wide Area Networks) – which are riddled with multiprotocol level switching (MPLS) connectivity, 4G and LTE wireless, and terrestrial broadband internet.
The data from network operational analysis was ever-increasing. The network team collected device telemetry, network flow records, synthetic traffic data, domain name system (DNS) records, syslogs, and data from other network management systems. Quite often, the team would correlate these classes of data manually.
Failure to tackle volumes of alerts
The team had six to eight different network performance management (NPM) tools, and they used four to five of them regularly. The data complexity resulted in a tool sprawl that fragmented workflows and insights, making the network operations team less productive. Legacy network operations tools produced volumes of unactionable alerts that were leading to alert fatigue and obscuring critical problems. They realized notifications from tools on static thresholds were failing to reveal the impact of alerts, let alone the root-causes. To compound issues, due to complex dependencies, the network operations teams received 100+ alarms because of a single network fault.
Administrators spent too much time sifting through the noise, looking for clues and patterns. To put that into perspective, 39% of network problems are reported by end-users before network teams could detect them. By the time network admins responded to issues, user productivity degraded, and business slowed. Not only the VP’s team but most network operations teams spend 75% of their time finding root-causes and fixing problems. Due to this constant firefight, network engineers have little time to focus on strategic projects.
The problem augmented when the Covid-19 outbreak hit companies across the globe. Organizations were forced to work from homes, and resources were stretched thin. Companies felt the pressure more than ever to keep network operations running smoothly.
Trust in AIOps-driven network performance management
Enterprise Management Associates (EMA) research found 92% of enterprises are interested in AIOps-driven NPM. 28% say it is already critical to network operations. Also, enterprises with over 5000 network devices are more likely to identify AIOps as essential to NPM. The network leader, too, started looking closely at the interaction of AIOps and network performance management. He realized that effective AIOps technology cuts through alert noise and binds all the pieces of his enterprise’s network together. AIOps-driven network performance monitoring correlates insights across all network domains, from user devices to software-defined wide area network overlay, and also into the cloud. It normalizes different classes of data, understands their dependencies, and minimizes false positives generated by various network performance management tools.
Weighing in the possibilities that AIOps can bring to the table, the VP called a meeting with his team members to discuss the way forward in this crisis. After a rigorous brainstorming session, he realized three major benefits of incorporating machine intelligence into networks.
- Automated remote network traffic analysis: The AIOps technology learns how a network behaves in a distributed working environment and how traffic traverses it. It identifies and alerts on anomalies, including indicators of security and performance issues.
- Self-healing networks: Machine learning algorithms automate root-cause analysis of network and application performance issues. Also, AIOps solutions for networks learn how to deduce and isolate the cause of a problem. Intelligent networks then explain the nature of that problem to network engineers. Experts, as a result, can skip the entire diagnostic process and use their skill set to deploy an immediate fix to the problem.
- Predictive capacity management: Artificial intelligence in network operations performs predictive and trend analysis to identify oversubscribed links and future bottlenecks. It can also suggest changes to mitigate capacity issues or roll out changes automatically.
Additionally, the VP concluded his enterprise could make use of AIOps in a distributed workplace to correlate events associated with device configuration change management, troubleshoot connectivity issues, upgrade firmware, and manage firewalls.
Application of network automation
Netenrich has been helping network operations leaders successfully implement AIOps in networks. We understand the application of automated changes based on machine insights as one of the top use cases for AIOps-driven network automation. We also know that for critical parts of the network, most organizations prefer to keep human expertise in the loop. Keeping all the factors in mind, our network intelligence drives self-healing networking while our network reliability engineers track the right signals and understand the network state.
As companies across the globe switch to remote operations during the crisis, we help them achieve a 50% reduction in configuration change failures, reduce risks, increase compliance, and roll out upgrades at the speed of software. Netenrich helps:
- Simplify device configuration management, automate incident resolutions, and protect vulnerable systems from zero-day threats.
- Gain expert assistance on network status, changes done, conflicts found, compliance status, and user activities on your network.
- Automate firmware upgrades and receive expert recommendations to remain up to date on the patches released from application software vendors and security risks.
- Identify hidden risks, protect against threats, and respond to incidents without taking a performance hit.
- Proactively identify and quickly fix LAN problems with preconfigured monitoring dashboards, visibility into compliance status, and up-to-date network reporting.
Do you think network leader’s story could be yours? You can’t leave your network operations to chance. Especially in the remote working model. You either upgrade your networks or live long enough to see legacy NetOps crumble. Let’s talk if you want to move towards a self-healing network.