Project Description:

Mobile cloud computing (MCC) bridges the gap between the limited capabilities of mobile devices and the increasing complexity of mobile applications, by offloading the computational workloads from local devices to the remote cloud. However, the effectiveness of mobile cloud computing could be impaired by the dynamic nature of system and network contexts, which lead to heterogeneous mobile application behaviors and seriously reduce the appropriateness of workload offloading decisions. This project exploits these critical dynamics in mobile clouds that are indispensable to efficient, prompt, and reliable workload offloading. More specifically, it addresses three closely intertwined research issues in mobile cloud computing. The first part investigates how to analytically formulate the stochastic characteristics of run-time application executions, based on which the workload offloading decisions are probabilistically made and systematic techniques are developed to practically enforce such decisions. The second part incorporates the contexts and performance requirements of mobile cloud applications into the design of wireless networks, so as to adaptively balance between the wireless energy cost and application performance in mobile clouds through fundamental redesign of wireless transmission scheduling algorithms. The third part focuses on testbed development to automatically investigate the run-time system and network dynamics of mobile cloud applications in practice. This testbed consists of off-the-shelf smartphones and wearable devices, and enables in-field experiments for evaluating the performance of the proposed techniques and system designs.

 

Selected publications (Complete List):

  1. Minimizing Context Migration in Mobile Code Offload [pdf]
    Yong Li and Wei Gao, IEEE Transactions on Mobile Computing, vol. 16(4), 2017, pp. 1005-1018.

  2. Interconnecting Heterogeneous Devices in the Personal Mobile Cloud [pdf]
    Yong Li and Wei Gao, in Proceedings of the 36th IEEE Conference on Computer Communications (INFOCOM), 2017.
    (Acceptance Ratio: 292/1395=20.9%)

  3. Scheduling Dynamic Wireless Networks with Limited Operations [pdf]
    Haoyang Lu and Wei Gao, in Proceedings of the 24th IEEE International Conference on Network Protocols (ICNP), 2016.
    (Acceptance Ratio: 46/229=20.1%)

  4. Supporting Real-Time Wireless Traffic through A High-Throughput Side Channel [pdf][slides]
    Haoyang Lu and Wei Gao, in Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2016.
    (Acceptance Ratio: 35/187=18.7%)

  5. Application-Aware Traffic Scheduling for Workload Offloading in Mobile Clouds [pdf][slides]
    Liang Tong and Wei Gao, in Proceedings of the 35th IEEE Conference on Computer Communications (INFOCOM), 2016.
    (Acceptance Ratio: 300/1644=18.25%)

 

Participants:

 

Software:

Under development. As project goes on, more information will be available.


This material is based upon work supported by the National Science Foundation under Grant No. 1456656. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).