Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics

Abstract To overcome with the computation limitation of resource-constrained wireless IoT edge devices, providing an efficient task offloading and resource allocation in distributed mobile computing environment is consider as a challenging promising solution. Hyper-heuristic recent times gaining popularity due to its general applicability same solution solve different types problems. generally heuristic method or framework which iteratively evaluates chooses best low-level heuristic, In this paper, we try device computing, non-convex NP-Hard problem by using proposed novel Hyper-Heuristic Framework Stochastic Heuristic Selection (HHFSHS) Contextual Multi-Armed Bandit (CMAB) Epsilon-Decreasing strategy, considering two key Quality Service (QoS) objectives time energy consumption. These multiobjective criteria are modeled single-objective optimization goal minimize latency consumption devices without losing pareto optimality. Finally, evaluate performance comparing other individual meta-heuristic algorithms.