Abstract
Mobile edge computing (MEC) has been an alternative to mobile cloud computing (MCC) for computationally intensive mobile tasks by offloading computations to nearby servers. However, it is not easy to generate an optimal offloading scheme considering both energy consumption and time delay with low time complexity. In this paper, we propose a lightweight energy-efficient computational offloading scheme (LEEOS) for a task to make the offloading decision of each component. First, LEEOS calculates the cost values of local execution and remote execution for all components. Based on these cost values, it uses a greedy heuristic to determine which components to offload to mobile edge servers for execution. Experiment results show that our proposed approach is promising in terms of energy consumption of user equipment as well as computation time.
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