How to manage the machine learning with the scalability of network management?
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Machine learning can be managed with the scalability of network management by using adaptive and robust approaches that leverage the capabilities of software-defined networking (SDN) . SDN allows for the separation of the data and control plane, providing flexibility in network management. However, the centralized nature of SDN can lead to scalability and single point of failure issues. To address this, multiple controllers can be used, and switches can be assigned autonomously based on network traffic patterns . Additionally, predictive-based approaches using machine learning techniques can be employed to anticipate scaling needs and make timely decisions with the right resources . These methods enhance network resource utilization and increase the scalability of SDN .
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| Papers (5) | Insight |
|---|---|
| The paper discusses managing the scalability of machine learning for network management by using a cloud-native architecture and evaluating its scalability using three different ML QoT predictors. | |
| The paper describes a method for managing network resources using machine learning to determine whether to adjust auto-scaling rules for application services. | |
| The paper proposes a predictive-based approach using machine learning techniques to manage network function capacity and scaling. It predicts scaling needs in advance and makes scaling decisions and actions at the right time with the right resources. | |
27 Jul 2020 | The paper discusses the use of machine learning in network resource management, including energy optimization and auto-scaling methods. However, it does not specifically address how to manage machine learning with the scalability of network management. |
01 Jan 2020 | The paper proposes an adaptable and robust network management approach using machine learning to enhance network resource utilization and increase scalability in software-defined networks. |
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