.png){kind=icon}
.png){kind=icon}
Return
A Two-Stage Hybrid Multi-Objective Optimization Evolutionary Algorithm for Computing Offloading in Sustainable Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/TCE.2024.3376930.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Edge computing is an effective complementary technology to cloud computing, allowing end devices to offload tasks onto edge base stations (BSs) to satisfy the quality of experience of consumers. Due to the limitation of storage and computing resources, a single BS cannot satisfy the heavy computing tasks. In this regard, multi-BS collaboration is an effective way to alleviate this issue. Moreover, service caching and cloud-edge collaboration computing also show attractive advantages in handling the surging data traffic. However, to the best of our knowledge, there is rarely work that consider all of the aforementioned scenarios simultaneously. To fill this research gap, this paper comprehensively considers the computing offloading problem in sustainable edge computing based on the above scenarios. Specifically, the computing offloading problem is first modeled as a multi-objective optimization problem with the purpose of minimizing the delay and energy consumption. Then, a two-stage hybrid multi-objective optimization evolutionary algorithm, called TH-MOEA, is designed to address the above formulated problem, which uses a novel competitive swarm optimizer to accelerate convergence in the early evolutionary stage and adopts a diversity-enhanced immune algorithm to improve diversity in the later evolutionary stage. Simulation results show that TH-MOEA outperforms several state-of-the-art peer methods.
Keywords:
Cloud computing
Task analysis
Optimization
Edge computing
Delays
Collaboration
Energy consumption
computing offloading
multiobjective optimization
evolutionary algorithm
Journal
IF:
10.9
Papers:
5.1K
Citations:
6.8K