Abstract
Mission critical services and applications with computation-intensive tasks require extremely low latency, while task offloading for mobile edge computing (MEC) incurs extra latency. In this work, the optimization of power consumption and delay are studied under ultra reliable and low latency (URLLC) framework in a multiuser MEC scenario. Delay and reliability are relying on users’ task queue lengths, which is attested by probabilistic constraints. Different from the current literature, we consider a comprehensive system model taking into account the effects of bandwidth, computation capability, and transmit power. By introducing the approach of Lyapunov stochastic optimization, the problem is solved by splitting the multi-objective optimization problem into three single optimization problems. Performance analysis is conducted for the proposed algorithm, which illustrates that the tradeoff parameter indicates the tradeoff between power and delay. Simulation results are presented to validate the theoretical analysis of the impact of various parameters and demonstrate the effectiveness of the proposed approach.
This work was supported in part by the National Natural Science Foundation of China (No. 61701168, 61832005, 61571303) and the Fundamental Research Funds for the Central Universities (No. 2019B15614).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yu, Y., Zhou, S., Lian, X., Tan, G., Mao, Y. (2019). Mobile Edge Computing-Enabled Resource Allocation for Ultra-Reliable and Low-Latency Communications. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_30
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DOI: https://doi.org/10.1007/978-3-030-32388-2_30
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