Blog :: NPMD

Machine learning takes the guesswork out of network capacity planning

It likely will come as no surprise that the machine learning (ML) market is expected to experience a compound annual growth rate of almost 40 percent through 2027 – when it’s estimated the value of the market will then be $117 billion. What may come as more of a surprise is that the top two ML use cases are risk management, followed by performance analysis and reporting. 

In fact, we’re seeing a significant increase in the number of companies that are looking for a network performance monitoring and diagnostic (NPMD) solution that incorporates ML. These forward-looking companies understand that network capacity forecasting needs ML functionality to account for current and future network trends. 

Organizations achieve several benefits when utilizing ML on an NPMD solution for their network capacity planning. Three of the top benefits are: 

  1. Predict resource utilization: The past two years have served as a stark reminder that corporate network traffic can be upended – repeatedly! – by factors far outside of IT’s control. Enterprises that once relied upon stable, predictable corporate network traffic patterns now understand that networks will likely never return to what they once were, thanks to things like remote work initiatives and the migration of corporate workloads to the cloud. The ability to measure resource utilization and predict when additional network capacity will be required is strategically important. Likewise, the ability to associate users, devices, and applications with bandwidth consumption enable the network team to understand the resource utilization and effect each has on the organization. 
  2. Improve application experience: A positive user experience requires an efficient, latency-free network environment that extends from the user all the way into the public cloud. The ability to measure application performance and contrast that against network capacity lets the NetOps team know when existing network resources are insufficient to support user needs. 
  3. Plan for network upgrades: Network teams must predict how long existing infrastructure will be able to support the dynamic needs of the business – as well as what’s needed to support future growth. Network upgrades are costly and require a strategic process that delivers advance warning for budget and resource planning. To accomplish this, the network team needs a way to predict how long the existing infrastructure will support the needs of the business and then understand what new resources will be required to support future growth. The ability to accurately forecast and optimize resource utilization based on real-world data is key to achieving a strong ROI. 

When Plixer’s NPMD platform is combined with artificial intelligence (AI) and ML, it creates an integrated solution that enables enterprises to reap those benefits – and more. Specifically, the solution: 

  • Breaks out risks into distinct categories that enable users to isolate high-risk endpoints.  
  • Provides detailed configuration and operating information for individual devices, including calculating risk scores for each endpoint. 
  • Monitors application performance to identify the cause of issues like packet loss using deep packet inspection (DPI) to monitor internal and cloud-bound critical application traffic. 
  • Dynamically predicts network capacity requirements and uses ML to monitor data volumes.  

Overall, our solution enables enterprises to identify future usage hotspots, forecast multiple classes of data, generate threshold boundaries, identify seasonal variances, and generate data supporting infrastructure planning. 

Read our latest white paper, the How Plixer Uses Machine Learning for Network Security and Performance whitepaper or schedule a demo today.