In the dynamic world of IT, traditional network monitoring approaches are no longer sufficient to manage the complexities of today’s networks—be they wired or wireless. To stay ahead of network events, IT administrators must shift from being reactive to adopting a proactive stance. This transition involves a comprehensive approach to network monitoring that includes forecasting future network requirements with the help of machine learning (ML) technology. In this blog, we will talk about how organizations are harnessing the power of ML to achieve network forecasting.
ML and IT infrastructure management makes for a winning combination
ML—known for its ability to construct algorithms and provide solutions without predefined rules—has found a perfect fit in the networking domain. Network operations and management, often plagued by manual errors and complex challenges, benefit immensely from the proactive approach of ML. With the ability to adapt to changing network dynamics and address emerging issues, ML has become a valuable ally in the world of networking.
What is network forecasting?
Network forecasting—coupled with ML—is the cornerstone of proactive network management. It equips organizations with the ability to anticipate network requirements accurately, preventing bottlenecks and optimizing network efficiency. Traditional network monitoring often fails to provide visibility into the dynamic nature of modern networks. In contrast, ML leverages metrics such as traffic data, throughput, and application availability to estimate necessary network performance.
Let’s delve into how ML enhances network forecasting for IT infrastructures:
1. Anomaly detection: ML excels at handling large volumes of data and identifying patterns that might be overlooked. This is crucial for detecting abnormal traffic behavior and addressing network performance issues promptly.
2. Capacity planning: In a business landscape that frequently changes, ML adapts to the dynamic nature of networks. By changing algorithms on the fly, it provides accurate forecasts, aids in inventory management, and cuts costs.
3. Traffic prediction: ML analyzes real-time statistics, user behaviors, and historical bandwidth usage to offer insights into network traffic patterns, helping with decision-making.
4. Performance forecasting: Understanding how efficiently your network operates and predicting optimal performance levels with new technologies is imperative. ML analyzes collected data to provide patterns for informed decision-making.
5. Storage forecasting: Network forecasting with ML goes beyond monitoring network and application access speed. It also provides storage forecasting, allowing you to predict when disk utilization, RAM, and memory usage may each reach critical levels.
OpManager Plus: A glimpse into the future of IT networking
OpManager Plus is enterprise network observability software that combines proactive and reactive approaches to address the challenges in today’s hybrid and cloud network environments. It offers:
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Availability and health monitoring
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Bandwidth monitoring
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IP address management
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Configuration management
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Firewall log management
A reliable observability solution is one that integrates seamlessly with your infrastructure, providing scalability, customization, and comprehensive advantages. With ManageEngine OpManager Plus, you can harness the full capabilities of your IT infrastructure. Explore the array of capabilities OpManager Plus offers by downloading a free, 30-day trial. Schedule a demo with our experts for a technical walk-through and get a price quote. Visit our extensive set of pages to take a deeper dive into observability and everything beyond that OpManager Plus has to offer.
