An Efficient Machine Learning-Based Resource Allocation Scheme for SDN-Enabled Fog Computing Environment

Abstract

Fog computing is an emerging technology which enables computing resources accessibility close to the end-users. It overcomes the drawbacks of available network bandwidth and delay in accessing the computing resources as observed in cloud computing environment. Resource allocation plays an important role in resource management in a fog computing environment. However, the existing traditional resource allocation techniques in fog computing do not guarantee less execution time, reduced energy consumption, and low latency requirements which is a pre-requisite for most of the modern fog computing-based applications. The complex fog computing environment requires a robust resource allocation technique to ensure the quality and optimal resource usage. Motivated from the aforementioned challenges and constraints, in this article, we propose a resource allocation technique for SDN-enabled fog computing with Collaborative Machine Learning (CML). The proposed CML model is integrated with the resource allocation technique for the SDN-enabled fog computing environment. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FogBus</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iFogSim</i> are deployed to test the results of the proposed technique using various performance evaluation metrics such as bandwidth usage, power consumption, latency, delay, and execution time. The results obtained are compared with other existing state-of-the-art techniques using the aforementioned performance evaluation metrics. The results obtained show that the proposed scheme reduces 19.35% processing time, 18.14% response time, and 25.29% time delay. Moreover, compared to the existing techniques, it reduces 21% execution time, 9% network usage, and 7% energy consumption.