Name
Abstract | ||
|---|---|---|
Mobile edge computing is an emerging computing paradigm to augment computational capabilities of mobile devices by offloading computation-intensive tasks from resource- constrained smart mobile device onto edge clouds nearby with potential computation capability. However, in general, edge clouds have limited computation resource and energy. Thus it is critical to achieve high energy efficiency while ensuring satisfactory user experience. In this paper, we first formulate the computation offloading problem for mobile edge computing into the system cost minimization problem by taking into account the completion time and energy. We then transform the optimization problem into a convex problem and propose a distributed algorithm consisting of offloading strategy selection, clock frequency configuration, transmission power allocation and channel rate scheduling. Finally, the experimental results show that our algorithm can achieve energy-efficient offloading performance compared to other existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/NAS.2018.8515731 | 2018 IEEE International Conference on Networking, Architecture and Storage (NAS) |
Keywords | Field | DocType |
Mobile edge computing,Energy-efficient,Convex optimization,M/M/n queue model | Scheduling (computing),Efficient energy use,Computer science,Computation offloading,Real-time computing,Distributed algorithm,Mobile device,Mobile edge computing,Optimization problem,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-8368-2 | 1 | 0.43 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyan Yu | 1 | 1 | 0.43 |
| Quyuan Wang | 2 | 13 | 2.99 |
| Song-Tao Guo | 3 | 392 | 57.76 |