Work offloading allows a mobile device, i.e., the client, to execute its computation-intensive code remotely on a more powerful server to improve its performance and to extend its battery life. However, the difference in instruction set architectures (ISAs) between the client and the server poses a great challenge to work offloading. Most of the existing solutions rely on language-level virtual machines to hide such differences. Therefore, they have to tie closely to the specific programming languages. Other approaches try to recompile the mobile applications to achieve the specific goal of offloading, so their applicability is limited to the availability of the source code. To overcome the above limitations, we propose to extend the capability of dynamic binary translation across clients and servers to offload the identified computation-intensive binary code regions automatically to the server at runtime. With this approach, the native binaries on the client can be offloaded to the server seamlessly without the limitations mentioned above. A prototype has been implemented using an existing retargetable dynamic binary translator. Experimental results show that our system achieves 1.93X speedup with 48.66% reduction in energy consumption for six realworld applications, and 1.62X speedup with 42.4% reduction in energy consumption for SPEC CINT2006 benchmarks.
|Original language||English (US)|
|Title of host publication||MobiSys 2017 - Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||13|
|State||Published - Jun 16 2017|
|Event||15th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2017 - Niagara Falls, United States|
Duration: Jun 19 2017 → Jun 23 2017
|Name||MobiSys 2017 - Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services|
|Conference||15th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2017|
|Period||6/19/17 → 6/23/17|
Bibliographical noteFunding Information:
We would like to thank Gang Shi and Minjun Wu for their help in setting up the experimental environment and collecting the experimental results. We are also very grateful to Geoffrey Challen and the anonymous reviewers for their valuable comments and feedback. This work is supported in part by the National Science Foundation under the grant number CNS-1514444.
© 2017 ACM.
Copyright 2017 Elsevier B.V., All rights reserved.
- Computation ofloading
- Dynamic binary optimization
- Dynamic binary translation
- Ofloading target selection