Online Collaborative Resource Allocation and Task Offloading for Multi-access Edge Computing
Geng Sun, Minghua Yuan, Zemin Sun, Jiacheng Wang, Hongyang Du, Dusit, Niyato, Zhu Han, and Dong In Kim

TL;DR
This paper proposes an online joint resource allocation and task offloading method for multi-access edge computing, aiming to minimize energy consumption while meeting delay constraints through an innovative edge-cloud collaborative architecture.
Contribution
It introduces a novel online optimization approach using Lyapunov framework and decomposition techniques for energy-efficient task offloading in MEC systems.
Findings
Achieves lower energy consumption compared to benchmarks.
Effectively manages delay constraints in dynamic MEC environments.
Demonstrates superior system performance through simulations.
Abstract
Multi-access edge computing (MEC) is emerging as a promising paradigm to provide flexible computing services close to user devices (UDs). However, meeting the computation-hungry and delay-sensitive demands of UDs faces several challenges, including the resource constraints of MEC servers, inherent dynamic and complex features in the MEC system, and difficulty in dealing with the time-coupled and decision-coupled optimization. In this work, we first present an edge-cloud collaborative MEC architecture, where the MEC servers and cloud collaboratively provide offloading services for UDs. Moreover, we formulate an energy-efficient and delay-aware optimization problem (EEDAOP) to minimize the energy consumption of UDs under the constraints of task deadlines and long-term queuing delays. Since the problem is proved to be non-convex mixed integer nonlinear programming (MINLP), we propose an…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Distributed and Parallel Computing Systems
