.png){kind=icon}
.png){kind=icon}
Return
Dependent tasks offloading based on particle swarm optimization algorithm in multi-access edge computing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2021.107790.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The proliferation of emerging applications such as augmented reality, face recognition, and autonomous driving have stimulated the growth of demand for low-latency services, while the traditional cloud computing paradigm inevitably increases end-to-end latency. Multi-access edge computing deploys computing and storage resources to user terminals, which is expected to become an effective solution. Most of the existing researches on multi-access edge computing focus on the offloading of independent tasks, which cannot meet the challenge of a real scenario in which a task is composed of multiple interdependent subtasks. To bridge the gap, we formulate the problem of offloading multi-dependent tasks in multi-access edge computing considering both task completion time and execution cost. Since this offloading problem is NP-hard, a queue-based improved multi-objective particle swarm optimization is proposed. During the optimization process, we introduce the Pareto optimal relationship and define a transition probability to obtain the optimal solution. A large number of simulation results show that compared with other alternatives, the performance of our algorithm can be improved by about 3%-25%. (C) 2021 Published by Elsevier B.V.
Keywords:
Multi-access edge computing
Task offloading
Pareto optimal relationship
Dependency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W