A HYBRID METAHEURISTIC OPTIMIZATION FRAMEWORK FOR  ENERGY-EFFICIENT RESOURCE ALLOCATION IN EDGE–CLOUD  COMPUTING ENVIRONMENTS: COMPUTATIONAL ANALYSIS AND  PERFORMANCE EVALUATION

Kasapaka Rubenraju,V. Ravikumar,N Srivani

A HYBRID METAHEURISTIC OPTIMIZATION FRAMEWORK FOR ENERGY-EFFICIENT RESOURCE ALLOCATION IN EDGE–CLOUD COMPUTING ENVIRONMENTS: COMPUTATIONAL ANALYSIS AND PERFORMANCE EVALUATION

Authors

Kasapaka Rubenraju,V. Ravikumar,N Srivani

Keywords:

Edge Computing, Cloud Computing, Resource Allocation, Metaheuristic Optimization, Energy Efficiency, Particle Swarm Optimization, Genetic Algorithm.

Abstract

The increasing adoption of Internet of Things (IoT) applications, real-time analytics, and latency-sensitive serviceshas accelerated the deployment of edge–cloud computing architectures. These environments combine the lowlatency capabilities of edge nodes with the scalable computational resources of cloud data centers. However, efficient resource allocation remains a significant challenge due to dynamic workloads

References

J. Kennedy and R. Eberhart, “Particle swarm optimization,” 1995.

J. H. Holland, Adaptation in Natural and Artificial Systems, 1992 Edition.

A. Beloglazov and R. Buyya, “Energy efficient resource management in cloud computing,” 2012.

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Published

2023-03-20

How to Cite

Kasapaka Rubenraju,V. Ravikumar,N Srivani. (2023). A HYBRID METAHEURISTIC OPTIMIZATION FRAMEWORK FOR ENERGY-EFFICIENT RESOURCE ALLOCATION IN EDGE–CLOUD COMPUTING ENVIRONMENTS: COMPUTATIONAL ANALYSIS AND PERFORMANCE EVALUATION. Journal of Computational Analysis and Applications (JoCAAA), 31(3), 1011–1018. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5553