A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks

• URL: http://arxiv.org/abs/2007.10102v1
• Date: Mon, 20 Jul 2020 13:46:42 GMT
• Title: A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks
• Authors: Sihua Wang, Mingzhe Chen, Xuanlin Liu, Changchuan Yin, Shuguang Cui, H. Vincent Poor
• Abstract summary: A joint task, spectrum, and transmit power allocation problem is investigated for a wireless network. The proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard Q-learning algorithm.
• Score: 108.57859531628264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, a joint task, spectrum, and transmit power allocation problem is investigated for a wireless network in which the base stations (BSs) are equipped with mobile edge computing (MEC) servers to jointly provide computational and communication services to users. Each user can request one computational task from three types of computational tasks. Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users. This problem is formulated as an optimization problem whose goal is to minimize the maximal computational and transmission delay among all users. A multi-stack reinforcement learning (RL) algorithm is developed to solve this problem. Using the proposed algorithm, each BS can record the historical resource allocation schemes and users' information in its multiple stacks to avoid learning the same resource allocation scheme and users' states, thus improving the convergence speed and learning efficiency. Simulation results illustrate that the proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard Q-learning algorithm.

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