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AI-Enabled Task Offloading for Improving Quality of Computational Experience in Ultra Dense Networks

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Published:14 March 2022Publication History
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Abstract

Multi-access edge computing (MEC) and ultra-dense networking (UDN) are recognized as two promising paradigms for future mobile networks that can be utilized to improve the spectrum efficiency and the quality of computational experience (QoCE). In this paper, we study the task offloading problem in an MEC-enabled UDN architecture with the aim to minimize the task duration while satisfying the energy budget constraints. Due to the dynamics associated with the environment and parameter uncertainty, designing an optimal task offloading algorithm is highly challenging. Consequently, we propose an online task offloading algorithm based on a state-of-the-art deep reinforcement learning (DRL) technique: asynchronous advantage actor-critic (A3C). It is worthy of remark that the proposed method requires neither instantaneous channel state information (CSI) nor prior knowledge of the computational capabilities of the base stations. Simulations show that the our method is able to learn a good offloading policy to obtain a near-optimal task allocation while meeting energy budget constraints of mobile devices in the UDN environment.

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          • Published in

            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 22, Issue 3
            August 2022
            631 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/3498359
            • Editor:
            • Ling Liu
            Issue’s Table of Contents

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 14 March 2022
            • Accepted: 1 October 2021
            • Revised: 1 March 2021
            • Received: 1 October 2020
            Published in toit Volume 22, Issue 3

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