Hybrid Energy-Efficient Task Offloading Algorithm (HEETA): A Framework for Optimizing Edge Computing Offloading Decisions

Abstract

In the evolving realm of Mobile Edge Computing (MEC), efficient task offloading remains pivotal. This paper introduces the Hybrid Energy-Efficient Task Offloading Algorithm (HEETA) to address the deficiencies of the current Joint Optimization Task Offloading Strategy based on Particle Swarm Optimization (JOBPSO). Drawing from a broad dataset encompassing diverse MEC operational variables, HEETA exhibits exemplary performance metrics, with a notable mean fitness of approximately 0.99984 and a minimal standard deviation of 0.000116. Such metrics not only reflect HEETA's robustness but also its adaptability across multifaceted MEC parameters. Furthermore, its dynamic nature facilitates adaptability to variables including task numbers, computational capacity, and latency constraints, resulting in marked improvements in energy efficiency. Quantitative evaluations, as evidenced by a performance matrix, position HEETA's global best fitness values between approximately 0.9998 and 0.9999. While HEETA signifies a monumental step in enhancing energy efficiency, prolonging device longevity, and optimizing overall MEC system performance, the research acknowledges potential limitations, emphasizing the imperatives of accurate modeling and subsequent validations within distinct MEC environments.