Machine Learning Models for Dynamic Load Balancing in Edge Computing

Authors: Perumallapalli Ravikumar

Country: United States

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Abstract: In edge computing, dynamic load balancing guarantees optimal resource allocation and lowers latency in distributed systems. In order to facilitate real-time decision-making and effective resource use, this work investigates the integration of machine learning models for adaptive load control in edge nodes. We go over the use of supervised, unsupervised, and reinforcement learning models designed to tackle particular issues in edge contexts, such as workload allocation that is dynamic, heterogeneous, and scalable. Throughput, latency, and system dependability have been significantly improved in experimental findings, establishing ML-based techniques as a key component of next edge computing developments.It explores supervised, unsupervised, and reinforcement learning methodologies, emphasizing how they are used in adaptive decision-making, workload prediction, and resource allocation. A comparison of ML algorithms, an assessment of their performance indicators, and a suggested architecture for incorporating ML-based load balancing into edge networks are some of the main contributions. The results show how ML-driven solutions may improve reaction times, reduce energy usage, and increase system efficiency, opening the door for resilient and flexible edge infrastructures. In order to address issues including resource heterogeneity, fluctuating task demands, and system scalability, this paper investigates machine learning (ML) models designed for dynamic load balancing in edge computing.

Keywords: Task Scheduling, Workload Prediction, Adaptive Systems, Machine Learning, Resource Allocation, Supervised Learning, Reinforcement Learning, Edge Computing, Dynamic Load Balancing, System Scalability

Paper Id: 231798

Published On: 2014-01-07

Published In: Volume 2, Issue 1, January-February 2014

Cite This: Machine Learning Models for Dynamic Load Balancing in Edge Computing - Perumallapalli Ravikumar - IJIRMPS Volume 2, Issue 1, January-February 2014.

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