A Multi-objective Task Offloading Strategy for Workflow Applications in Mobile Edge-Cloud Computing

Abstract

Prompted by the remarkable progress in both edge computing and cloud computing, mobile edge-cloud computing has become a promising computing paradigm, where mobile users may take advantages of the low-latency property of edge computing and the rich-resource capacity of cloud computing to provide a high quality of service for mobile applications. In mobile edge-cloud computing, a main challenge is how to efficiently offload workflow tasks in order to reduce energy consumption, and improve performance and reliability. In this paper, we formulate workflow offloading into a constrained multi-objective optimization problem and develop a hybrid algorithm involving differential evolution algorithm, artificial bee colony optimization and decoding heuristic to explore the optimal strategy of task offloading for workflow applications in mobile edge-cloud computing. The effectiveness of our strategy is evaluated by extensive simulations using real-world workflows. The results show that our strategy performs better than the state-of-the-art methods applied to similar problems.