Network forecasting powered by machine learning: The future-ready approach to bandwidth monitoring

IT administrators today no longer need a traditional network monitoring approach, given the complexities that today's networks(wired or wireless) bring. Network operators need to be proactive rather than being reactive on network events.They should consider a spectrum of monitoring aspects such as reporting, to predicting what the network needs in the future. This requires a promising technology to analyze, and forecast the network requirements data accurately and prevent bottlenecks or downtime.

For example, real time network performance monitoring should be followed by estimating what the network needs in the future. But the dynamic nature of every network makes it difficult to estimate a standard value, and hence requires solutions that can work on existing trends and present insights to maintain the network at an optimal level. In these cases, machine learning will help by collecting metrics such as traffic data, throughput, and application availability and estimate the necessary network performance. In this page, we will look at how organizations are achieving this.

Machine learning and networking, the future-ready duo for modern networks

Machine learning technology have made breakthrough in most industries to solve challenges. Since it relies on constructing algorithms, and creating solutions without pre-defined rules, tech enthusiasts use it for providing results for difficult situations. Networking is one particular domain where machine learning has major applicability to turn data into actionable insights.

Network operations and management is a vast avenue bound with most manual errors and difficulties. With problems threatening the organizations to stay vigilant, finding their equivalent solutions is just as complicated. Most modern network challenges depend on powerful technology like machine learning and a strategy such as a proactive approach to tackle any challenges that works well, come what may.

Network forecasting coupled with machine learning is a part of proactive network management that an organization would need to stay updated about avoiding bottlenecks and increasing the efficiency of the network. Although network forecasting is a benefit in itself for enterprises, how you forecast your existing IT infrastructure's needs such that it shows you visibility into the network, and still provide insights into the future network is where machine learning provides an edge.

How machine learning elevates network forecasting for IT infrastructures

  • Anomaly detection: Unlike manual analysis, machine learning can handle large volumes of data, and discover patterns with accuracy that might be overlooked. Organizations that handle more data have problems such as abnormal traffic behavior, and with machine learning, they will be able to detect anomalies or understand where the network performance lags behind.
  • Capacity planning: Business landscapes are bound to change more often, and can make it difficult for the administrators to keep track of their behavior needed for a good user experience. Machine learning can adopt to this dynamic nature of the network and provide accurate forecast reports by changing algorithms on their own. Therefore, it gives accurate predictions, helps in inventory management and in cutting costs.
  • Traffic prediction: In a network where the total throughput and bandwidth is pre-allocated, there can be cases when the network is being over utilized or where the network suffers bottlenecks. With machine learning, the statistics of those users accessing the network, the volume of data transferred with time-based patterns, and historical bandwidth usage of every user will be presented based in real time for easier decision-making.
  • Performance forecasting: How your network's performance efficiency is discovered to expect optimal operations level with new technologies is mandatory. Performance forecast trends can be used to analyze the data collected to provide you patterns that can help take informed decisions whether yours is a cloud or on-premise network infrastructure.
  • Storage forecasting: Planning changes or necessary network updates goes beyond monitoring and predicting how fast a user can access a network or an application. Network forecasting with machine learning provides storage forecasting to help you predict when the disc utilization, RAM and Memory usage can reach more than 80%, 90% or 100% based on historical utilization. Therefore, you can be ahead in planning the IT storage.

Working of machine learning and network forecasting

  • The forecasting tool collects the network traffic data and prepares them for analysis.
  • The pre processed data will then be used to choose the algorithm that needs to be used.
  • The patterns are created with collected data.
  • The defined algorithm validates the real time data with predicted data and gives the user a meaningful analysis which helps the network admin to understand and take precautionary actions better.



Consider an example where machine learning can help with network forecasting. An organization's network has been suffering with congestion issues. When the admin considers bandwidth upgrades, he suspects that the existing bandwidth might not be sufficient. So he uses forecast reports for its intelligence to see the traffic reports of the protocols, and plan to increase the bandwidth as business grows.

With machine learning, he was able to classify the type of traffic and estimate how much bandwidth to allocate to individual applications. The admin also decided to limit the usage of resource-intensive applications with medianet traffic reports, and were able to determine the optimal traffic ratio to maintain the expected performance.

The need of the hour!

How you keep your network future-ready is important, given the fast and powerful adoption of cloud, IoT and NaaS technologies. What if the organization needs different tools to support all technologies? Definitely, this is the most difficult call for a network admin, since the integration, flexibility and learning curve will be at their extremities. For the very reason, a tool that is compatible for any sizes and shapes is important.

OpManager Plus's network forecasting: Gain full-fledged observability into the future of your IT landscape

OpManager Plus is an enterprise network observability software which follows both proactive and reactive approach keeping observability and optimization benefits in the front and center. As a holistic IT operations management software, it addresses all your network management concerns found in today's hybrid and cloud network environments.

A reliable observability solution is one which brings all the potential advantages to let you integrate with your infrastructure. It should be scalable, customizable and bring all possible advantages under one name. With ManageEngine OpManager Plus, you can utilize and maximize the capabilities of your IT infrastructure!

Bring ML to your IT infrastructure with OpManager Plus!

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