Name
Abstract | ||
|---|---|---|
Mobile data offloading has already appeared to offer the means of addressing the challenges of limited computing capability and battery life of mobile devices. Most existing code offloading frameworks only consider migrating an application within a single network at a time and failed to fully utilize the energy efficiency mechanism of the latest CPU chips. To tackle these issues, we propose an offloading approach under both a multitasking environment and a heterogeneous network to increase energy and execution efficiency. The offloading problem was formulated as a biobjective optimization problem that aims to save energy and keep a good performance by combining mobile cloud computing with big.LITTLE technology under a heterogeneous network equipped with cellular and WiFi connectivity. By varying the applications and network scenarios, the experimental results show that with offloading a single application using our proposed framework, the data-intensive application can obviously benefit when the access point density of WiFi reaches 0.0002 unit/m(2). Under multiple application scenarios, our proposed framework can increase processing speed by an average of 0.3x over the single application and can save an average of 25% power over the single application. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1002/cpe.5128 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE |
Keywords | DocType | Volume |
big, LITTLE, code offloading, heterogeneous network, multitask | Journal | 33 |
Issue | ISSN | Citations |
7 | 1532-0626 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Lu | 1 | 38 | 6.88 |
| Ruoxue Liu | 2 | 0 | 0.34 |
| Wei Li | 3 | 227 | 25.46 |
| Hai Jin | 4 | 6544 | 644.63 |
| albert y zomaya | 5 | 427 | 43.75 |