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.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 61662052, in part by the Inner Mongolia Science and Technology Innovation Team of Cloud Computing and Software Engineering, in part by the Inner Mongolia Application Technology Research and Development Funding Project, and in part by the Major Research Plan of Inner Mongolia Natural Science Foundation of China under Grant 2019ZD15.
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Gao, Y., Yan, D. (2021). A Multi-objective Task Offloading Strategy for Workflow Applications in Mobile Edge-Cloud Computing. In: Ning, L., Chau, V., Lau, F. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2020. Communications in Computer and Information Science, vol 1362. Springer, Singapore. https://doi.org/10.1007/978-981-16-0010-4_32
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DOI: https://doi.org/10.1007/978-981-16-0010-4_32
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