Long-Term Max-Min Fairness Guarantee Mechanism for Integrated Multi-RAT and MEC Networks.
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| Title: | Long-Term Max-Min Fairness Guarantee Mechanism for Integrated Multi-RAT and MEC Networks. |
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| Authors: | Jing, Zewei, Yang, Qinghai, Qin, Meng, Li, Jinglei, Kwak, Kyung Sup |
| Source: | IEEE Transactions on Vehicular Technology; Mar2021, Vol. 70 Issue 3, p2478-2492, 15p |
| Subject Terms: | FAIRNESS, SPECTRUM allocation, MOBILE computing, RADIO technology, EDGE computing, MULTICASTING (Computer networks) |
| Abstract: | Recently, mobile edge computing (MEC) has been recognized as an emerging paradigm to meet the ever-increasing computation demands. Besides, multiple radio access technology (multi-RAT) has been proposed to enhance the network throughput and service reliability. However, the two technologies have been evolving independently. In this paper, we will develop an integrated multi-RAT and MEC network framework, which enables smart devices (SDs) to offload computation tasks over multiple RAT links in parallel. Considering the resource-limited nature as well as the time-varying property of the proposed network, we then focus on investigating long-term fairness guarantee mechanisms to facilitate fair resource sharing/allocation between SDs. Specifically, we formulate a max-min stochastic optimization problem with the objective of maximizing the minimum long-term time-average offloading utility. An adaptive task splitting and resource allocation algorithm is proposed based on the Lyapunov optimization technology, which jointly optimizes the SD task splitting and uplink transmit power allocation, the RAT subcarrier allocation, and the MEC computation frequency allocation. The adaptive algorithm can accommodate to the time-varying network dynamics without requiring their distribution information. Moreover, the proposed algorithm is shown to have polynomial computation complexity and be asymptotically optimal by rigorous analysis. Simulation results verify the theoretical analysis and show that the Jain's fairness index of the proposed algorithm converges to 1 as the number of time-slots grows, which outperforms the benchmark algorithms. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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