Name
Title | ||
|---|---|---|
Computation Offloading for Workflow in Mobile Edge Computing Based on Deep Q-Learning |
Abstract | ||
|---|---|---|
Mobile edge computing (MEC) can significantly enhance device computing power by offloading service workflows from mobile device computing to mobile network edges. Thus how to implement an efficient computation offloading mechanism is a major challenge nowadays. For the purpose of addressing this problem, this paper aims to reduce application completion time and energy consumption of user device (UD) in offloading. The algorithm proposed formalizes the computation offloading problem into an energy and time optimization problem according to user experience, and obtains the optimal cost strategy on the basis of deep Q-learning (DQN). The simulation results show that comparing to the known local execution algorithm and random offloading algorithm, the computation offloading algorithm proposed in this paper can significantly reduce the energy consumption and shorten the completion time of service workflow execution. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WOCC.2019.8770689 | 2019 28th Wireless and Optical Communications Conference (WOCC) |
Keywords | Field | DocType |
Mobile edge computing,energy consumption,completion time,computation offloading,deep Q-learning | Computer science,Server,Computer network,Computation offloading,Mobile device,Mobile edge computing,Cellular network,Energy consumption,Workflow,Optimization problem,Distributed computing | Conference |
ISSN | ISBN | Citations |
2379-1268 | 978-1-7281-0661-8 | 0 |
PageRank | References | Authors |
0.34 | 6 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anqi Zhu | 1 | 4 | 3.09 |
| Song-Tao Guo | 2 | 392 | 57.76 |
| Mingfang Ma | 3 | 3 | 2.74 |
| Hao Feng | 4 | 99 | 20.70 |
| Bei Liu | 5 | 26 | 12.94 |
| Xin Su | 6 | 283 | 53.83 |
| Minghong Guo | 7 | 0 | 0.34 |
| Qiucen Jiang | 8 | 0 | 1.35 |