Name
Abstract | ||
|---|---|---|
Task offloading in edge computing has become an effective way to expand the computing power of user equipment, since it migrates computing-intensive applications from user equipment to edge servers. The execution of a task may require multiple services. Today, many works study the edge computing about service placement or migration with single service tasks. However, it may not meet the need of applications on large scale. In this paper, we study a computational offloading method for multi-service tasks. Here, the execution of each task requires the collaboration of multiple services, and each service is indispensable. Specifically, we design an evaluation metric about system cost, and aim to find the decision to minimize this metric to solve the mobile edge computing (MEC) problem with multi-services tasks. Since this problem is NP-hard, we design the multi-service task computing offload algorithm (MTCOA) to realize the optimal solution. The simulation results show that the algorithm can effectively reduce the cost of computing offloading, and it has higher resource utilization than the existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s10489-021-02549-2 | APPLIED INTELLIGENCE |
Keywords | DocType | Volume |
Multi-service task, Cost-efficient, Genetic | Journal | 52 |
Issue | ISSN | Citations |
4 | 0924-669X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shudian Song | 1 | 0 | 0.34 |
| Shuyue Ma | 2 | 0 | 2.37 |
| Jingmei Zhao | 3 | 0 | 0.34 |
| Feng Yang | 4 | 0 | 0.34 |
| Linbo Zhai | 5 | 15 | 7.01 |