Performance management goes by a handful of monikers — performance monitoring, assurance, visibility— all meaning the same thing. The performance management definition is the process and the tools in place to analyze network and/or application data and to conduct root-cause analysis that locates issues that impact a user’s experience.
Currently, IT departments rely on three performance management tools to catch performance concerns. Application performance management (APM) monitors an application’s performance. Network performance management (NPM) inspects network’s data to find any blockage in the network traffic. And, lastly, unified performance management (UPM), which is a converged approach to APM and NPM. It provides IT professionals with end-to-end visibility into both the network and the applications as those technologies are becoming more interdependent upon each other.
How Performance Management is Advancing
The networking landscape requires performance management tools to evolve in order to complete its objectives. Complex technologies such as cloud computing and hybrid data center architectures are making this necessary. Another consideration is the fact that the number of users connected to the internet has grown exponentially. Similarly, the growth of the Internet of Things (IoT) devices has increased as well. Gartner predicts more than 20.4 billion IoT devices will be in use by 2020. All of these factors generate copious amount of data that overburdens IT departments’ capacity to monitor. The future prediction is that artificial intelligence (AI) and machine learning (ML) will replace humans in resolving issues those technologies detect, freeing up IT’s time to handle other tasks.
Automation will play a pivotal role in the future of performance management tools. With the assistance of AI and ML, performance management processes should automatically detect performance roadblocks. Mark Milinkovich, director of product marketing at LiveAction, noted that AI and ML “will enable the network to continuously learn, spot, and address abnormalities in network traffic and dynamically adjust network policies to account for changes in usage or user behavior.” Milinkovich’s point means that those technologies will learn from past performance management issues to detect patterns in events that cause performance issues.