A New Offloading Method in Cloud Platforms: Reducing Latency Based on Data Compression

This paper introduces a novel approach to reduce computational latencies in cloud platforms by integrating data compression with Non-Orthogonal Multiple Access (NOMA) technologies. We propose a computational task offloading scheme that optimizes resource allocation to minimize maximum user task completion latency by utilizing an adaptive step size Artificial Fish Swarm Algorithm. Experiments conducted using Python simulation demonstrate that our approach significantly lowers task completion latencies compared to existing methods that rely solely on either data compression or NOMA technologies.