Abstract
Multi-Access Edge Computing (MEC) is a promising paradigm that providing cloud-like service for handling the high-complexity and latency-sensitive applications on user equipment (UE) via computation offloading. However, the execution reliability is rarely considered in current MEC studies, which is an important factor to guarantee the quality of service (QoS). For that, this paper considers an energy-saving offloading to satisfy the reliability and latency requirements of the application. Specifically, we formulate an optimization problem to minimize the UE's energy consumption with reliability and latency constraints. To tackle this NP-hard problem, we first divide the entire application into multiple directed-acyclic-graph-(DAG)-based subtasks, where the subtask can be executed on the UE locally or MEC server remotely. Then, we decompose the overall reliability and latency requirements into multiple constraints for each subtask. Finally, we propose a fast heuristic algorithm to find a solution satisfying the constraints. Simulation results demonstrate the proposed algorithm obtains lower energy consumption compared with the local execution and random assignment and costs less runtime compared with the greedy algorithm.
Highlights
In recent years, with the development and popularity of mobile devices and mobile Internet traffic [1], [2], more and more applications, such as augmented reality, virtual reality, face recognition, mobile healthcare, and interactive games, gradually go deep into our lives [3], [4]
Due to the limited battery capacity and computation resources [5], it is difficult for the mobile devices to run the high-complexity applications with a satisfactory quality of experience (QoE) and quality of service (QoS)
Cloud Computing provides a solution to this problem, it leads to the heavy load and congestion in the network which influences the latencysensitive applications [4]
Summary
With the development and popularity of mobile devices and mobile Internet traffic [1], [2], more and more applications, such as augmented reality, virtual reality, face recognition, mobile healthcare, and interactive games, gradually go deep into our lives [3], [4]. The main contributions of our work are summarized as follows: 1) To guarantee the satisfactory reliability of the entire task of an application, it is divided into multiple subtasks that can be offloaded to the MEC servers for execution. It avoids the repeating execution of the task in a single UE for the reliability and would save the UE’s energy.
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