In a timeframe as short as the last 5 years, enterprise networks have become increasingly complex. Not only have the number of network types and devices that require real-time monitoring multiplied, but network virtualization use has accelerated and will continue to do so. According to Allied Market Research, the Network Function Virtualization (NFV) market was $26B in 2022 and is on target to grow to $180B by 2031. Although this virtualization has a host of benefits for organizations, it does pose some serious performance management and monitoring challenges.
Why Has Network Monitoring Become So Difficult?
There has been a sea change in network monitoring solutions in recent years, and the task has become much more difficult for network engineers and enterprises. Imagine you’re a police officer tasked with monitoring a regular two-lane highway for speeding vehicles. The job is relatively simple: just set a speed checkpoint to monitor the speed of each passing car, and ticket each one going over the speed limit.
Now imagine you must shift to monitoring a 16-lane highway. This means tracking speed, poor lane changes, heavy vehicles, texting drivers, and more. Throw in changing technology (autonomous cars, ride-sharing vehicles, E-scooters), all with different rules, and the job becomes infinitely more complex.
This is what modern network monitoring entails. More enterprises are employing multiple cloud services for their daily processes and boast their own data centers and applications used to communicate across all levels of their organization. On top of this, work from home and hybrid solutions open the door for network connections from multiple WAN locations. This means that the final end-user experience is based on dozens, or hundreds, of interconnected services that all need to function at peak performance levels.
Unfortunately, due to the complexity of networks, the variety of applications (traditional, SaaS, microservices), in addition to the new types of devices and networks being added (5G, 6G, IoT devices), there’s a lack of holistic network solutions that can monitor everything. This means enterprises can use dozens of physical and virtual monitoring tools. When network performance problems arise, they become tough to troubleshoot, resulting in costly downtime and lost productivity.
Gaining Control Requires a Unified Intelligent Approach to Monitoring
Due to the complexities of modern networks, successful management and monitoring requires comprehensive software capable of collating all the data from your network devices, including virtual networks, IoT devices, and users. On top of this, the software needs to have the capability of understanding and analyzing the data via the use of advanced analytics to clearly and quickly identify real-time network errors. Lastly, it must be designed to present this type of information concisely to IT teams and network operators. This way, the information can be shared and accessed across all levels of an organization for troubleshooting.
Coupling the above requirements with advanced AI will help the software intuitively learn about your normal network operations and trends to more rapidly identify when something is wrong. Also, the ideal dynamic monitoring solution should be equipped with upstream monitoring capability to provide information to system-wide observability and performance management services.
In Our Experience, IBM SevOne Meets All of These Requirements
A leading platform that meets the criteria for today’s most complex and dynamic networks is IBM SevOne. Its capabilities enable it to collate data from thousands of vendor sources, spanning both virtual and physical networks, and it functions with nearly any device. Its innovative machine learning (ML) analytics engine gives managers and IT teams intelligent, relevant insights.
In terms of AI solutions, IBM SevOne employs Watson AI, enabling it to better understand network performance root causes and predict future network problems. The network platform is designed to be user-friendly and easily accessible, helping users digest important information and act quickly in a business-critical situation. It also integrates with all of today’s leading IT service management systems.
These capabilities allow network operators to gain a quick but comprehensive understanding of what’s happening in their network. It takes all the incoming data and generates a unified view with intuitive dashboards and visualizations without overwhelming users with an ocean of alerts. Support teams are quicker to become aware of a problem and move to problem identification in seconds vs. the hours it can take today. Issues can also be resolved faster with built-in troubleshooting workflows.
As the built-in AI support learns about the organization’s regular network performance, and correlates that with other data like support tickets, it gets better at providing alerts to IT teams in advance of a performance issue occurring. Thus, allowing support teams to resolve problems before end users even see them. The result is better-performing networks and reduced downtime or productivity loss, even in the face of growing network complexity.
Just like the police officer tasked with monitoring that stretch of highway, whether it be 16 lanes or more, network operators have the complicated task of preventing or minimizing issues that draw user productivity to a screeching halt. Although the job has become increasingly complex over the years, IBM SevOne gives operators a tool to overcome all the obstacles they face now and in the future.