Joint partial computation offloading and resource allocation in MEC-enable networks

The sudden surge of various applications poses great challenges to the computation capability of mobile devices. To address this issue, computation offloading to multi-access edge computing (MEC) was proposed as a promising paradigm. This paper studies partial computation offloading scenario by considering time delay and energy consumption, where the task can be splitted into several blocks and computed both in local devices and MEC, respectively. Since the formulated problem is a nonconvex problem, this paper proposes an ant colony-based algorithm to achieve the suboptimal solution. Specifically, the proposed method first establish a multi-user one- MEC scenario, in which user devices are able to offload some part of the task to MEC server. Then, it develops an ant colony-based algorithm to decide the offloading parts and allocation strategy of MEC resources to minimize system cost. Finally, simulation results show the effectiveness of the proposed algorithm in terms of system cost and demonstrate that it outperforms other existing methods. © 2023, Beijing University of Posts and Telecommunications. All rights reserved.