Latency-minimized Computation Offloading in Fog Computing with Hybrid Whale optimization

Fog computing extends a paradigm of cloud computing to a network edge, thus reducing transmission delay of users’ tasks and providing fast services. Dramatic increase of mobile devices brings a big challenge of how to keep low-response processing of tasks. To solve this challenge, this work aims to minimize the latency of tasks while meeting energy limits of mobile devices, and formulates a constrained optimization problem. It designs an improved optimization algorithm by following core steps of whale optimization algorithm (WOA) inspired by whale hunting behaviors. It is named C haotic D ifferential W OA with Lévy flight (CDWL). CDWL integrates merits of WOA, chaotic differential evolution, and random walks of Lévy flight. In this way, CDWL achieves strong global search and quick convergence. Real-life experiments demonstrate CDWL outperforms its six state-of-the-art peers.