Abstract
Mobile-edge computing (MEC) has become a popular research topic from both academia and industry since it can alleviate the computation and power limitations of mobile devices by offloading computation-intensive and energy-consuming tasks from mobile users to nearby edge servers for remote execution. Existing papers have studied related problems, however, none of them considers the reliability of MEC systems that may suffer soft errors during execution and bit errors during offloading. In this work, we study the task offloading and scheduling problem targeting to maximize the quality of experience (QoE) of multi-user MEC systems under a certain reliability requirement. We propose to decompose the original problem into i) a task offloading optimization problem, ii) a task-to-server assignment problem for ensuring system reliability constraint, and iii) a computing resource allocation problem for maximizing system QoE. To address these sub-problems, we first obtain the optimal offloading decision using the discrete particle swarm optimization method. We then propose a reliability-optimality analysis-based task assignment heuristic and a utility-optimal resource allocation algorithm. Simulation results show that our scheme outperforms two state-of-the-art approaches and two baseline methods. The average improvement on QoE (quantified by offloading utility) achieved by our scheme is up to 63.2% under reliability requirement.
Junlong Zhou is the corresponding author
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References
Bhat, G., Bagewadi, K., Lee, H.G., Ogras, U.Y.: REAP: runtime energy-accuracy optimization for energy harvesting IoT devices. In: ACM/IEEE DAC, pp. 1–6 (2019)
Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., Sabella, D.: On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration. In: IEEE CMOST, pp. 1657–1681 (2017)
Kuang, Z., Li, L., Gao, J., Zhao, L., Liu, A.: Partial offloading scheduling and power allocation for mobile edge computing systems. In: IEEE IoT, pp. 6774–6785 (2019)
Wang, J., Hu, J., Min, G., Zomaya, A.Y., Georgalas, N.: Fast adaptive task offloading in edge computing based on meta reinforcement learning. In: IEEE TPDS, pp. 242–253 (2021)
Chang, Z., Liu, L., Guo, X., Sheng, Q.: Dynamic resource allocation and computation offloading for IoT fog computing system. In: IEEE TII, pp. 3348–3357 (2021)
Gupta, S., Chakareski, J.: Lifetime maximization in mobile edge computing networks. In: IEEE TVT, pp. 3310–3321 (2020)
Tran, T.X., Pompili, D.: Joint task offloading and resource allocation for multi-server mobile-edge computing networks. In: IEEE TVT, pp. 856–868 (2019)
Salehi, M.: DRVS: power-efficient reliability management through dynamic redundancy and voltage scaling under variations. In: IEEE ISLPED, pp. 225–230 (2015)
Choi, Y., Park, S., Bahk, S.: Multi channel random access in OFDMA wireless networks. In: IEEE JSAC, pp. 603–613 (2006)
Liao, Z., Peng, J., Huang, J., Wang, J., Sharma, P.K., Ghosh, U.: Distributed probabilistic offloading in edge computing for 6G-Enabled massive internet of things. In: IEEE IoT, pp. 5298–5308 (2021)
Li, L., Cong, P., Cao, K., Zhou, J.: Feedback control of real-time EtherCAT networks for reliability enhancement in CPS. In: ACM/IEEE DATE, pp. 688–693 (2018)
Zhou, J., Hu, X.S., Ma, Y., Sun, J., Wei, T., Hu, S.: Improving availability of multicore real-time systems suffering both permanent and transient faults. IEEE TC 68(12), 1785–1801 (2019)
Zhou, J., Cao, K., Zhou, X., Chen, M., Wei, T., Hu, S.: Throughput-conscious energy allocation and reliability-aware task assignment for renewable powered in-situ server systems. IEEE TCAD 41(3), 516–529 (2022)
Zhao, Z.: A novel framework of three-hierarchical offloading optimization for MEC in industrial IoT networks. In: IEEE TII, pp. 5424–5434 (2020)
Zhang, Y., Zhou, J., Sun, L., Mao, J., Sun, J.: A novel firefly algorithm for scheduling bag-of-tasks applications under budget constraints on hybrid clouds. IEEE Access 7, 151888–151901 (2019)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62172224, in part by the Natural Science Foundation of Jiangsu Province under Grant No. BK20220138, in part by the China Postdoctoral Science Foundation under Grant Nos. BX2021128, 2021T140327, 2020M680068, in part by the Fundamental Research Funds for the Central Universities under Grant Nos. 30922010318 and 30922010406, in part by the Postdoctoral Science Foundation of Jiangsu Province under Grant No. 2021K066A, in part by the Open Research Fund of the State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences under Grant No. CARCHA202105, in part by the Future Network Scientific Research Fund Project under Grant No. FNSRFP-2021-YB-6, and in part by the Open Research Fund of Engineering Research Center of Software/Hardware Co-Design Technology and Application, Ministry of Education (East China Normal University) under Grant No. OP202203.
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Jiang, W., Zhou, J., Cong, P., Zhang, G., Hu, S. (2022). QoE and Reliability-Aware Task Scheduling for Multi-user Mobile-Edge Computing. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_32
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