Abstract
Mobile edge computing has become a key technology in IoT and 5G networks, which provides cloud-computing services in the edge of the mobile access network to realize the flexible use of computing and storage resources. While most existing research focuses on network optimization in small-scale scenes, this paper jointly considers the resources scheduling of servers, channels and powers for mobile users to minimize the system energy consumption. It’s an NP-hard problem which can only be solved through the exhaustive search with complexity of exponential level. The lightweight distributed algorithm proposed in this paper based on the Markov approximation framework can make the system converge to an approximate optimal solution with only linear level complexity. The simulation results show that the proposed algorithm is able to generate near-optimal solutions and outperform other benchmark algorithms.
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Acknowledgment
This work was funded by the Beijing Intelligent Logistics System Collaborative Innovation Center under Grant No. BILSCIC-2019KF-10, and the Fundamental Research Funds for the Central Universities under Grant No. 2019JBM027.
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Chen, H., Liu, M., Wang, Y., Fang, W., Ding, Y. (2019). A Markov Approximation Algorithm for Computation Offloading and Resource Scheduling in Mobile Edge Computing. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_1
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DOI: https://doi.org/10.1007/978-981-15-1925-3_1
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