TY - GEN
T1 - On exploiting dynamic execution patterns for workload offloading in mobile cloud applications
AU - Gao, Wei
AU - Li, Yong
AU - Lu, Haoyang
AU - Wang, Ting
AU - Liu, Cong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/9
Y1 - 2014/12/9
N2 - Mobile Cloud Computing (MCC) bridges the gap between limited capabilities of mobile devices and the increasing users' demand of mobile multimedia applications, by offloading the computational workloads from local devices to the remote cloud. Current MCC research focuses on making offloading decisions over different methods of a MCC application, but may inappropriately increase the energy consumption if having transmitted a large amount of program states over expensive wireless channels. Limited research has been done on avoiding such energy waste by exploiting the dynamic patterns of applications' run-time execution for workload offloading. In this paper, we adaptively offload the local computational workload with respect to the run-time application dynamics. Our basic idea is to formulate the dynamic executions of user applications using a semi-Markov model, and to further make offloading decisions based on probabilistic estimations of the offloading operation's energy saving. Such estimation is motivated by experimental investigations over practical smart phone applications, and then builds on analytical modeling of methods' execution times and offloading expenses. Systematic evaluations show that our scheme significantly improves the efficiency of workload offloading compared to existing schemes over various smart phone applications.
AB - Mobile Cloud Computing (MCC) bridges the gap between limited capabilities of mobile devices and the increasing users' demand of mobile multimedia applications, by offloading the computational workloads from local devices to the remote cloud. Current MCC research focuses on making offloading decisions over different methods of a MCC application, but may inappropriately increase the energy consumption if having transmitted a large amount of program states over expensive wireless channels. Limited research has been done on avoiding such energy waste by exploiting the dynamic patterns of applications' run-time execution for workload offloading. In this paper, we adaptively offload the local computational workload with respect to the run-time application dynamics. Our basic idea is to formulate the dynamic executions of user applications using a semi-Markov model, and to further make offloading decisions based on probabilistic estimations of the offloading operation's energy saving. Such estimation is motivated by experimental investigations over practical smart phone applications, and then builds on analytical modeling of methods' execution times and offloading expenses. Systematic evaluations show that our scheme significantly improves the efficiency of workload offloading compared to existing schemes over various smart phone applications.
UR - http://www.scopus.com/inward/record.url?scp=84920058531&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84920058531&partnerID=8YFLogxK
U2 - 10.1109/ICNP.2014.22
DO - 10.1109/ICNP.2014.22
M3 - Conference contribution
AN - SCOPUS:84920058531
T3 - Proceedings - International Conference on Network Protocols, ICNP
SP - 1
EP - 12
BT - Proceedings - IEEE 22nd International
PB - IEEE Computer Society
T2 - 22nd IEEE International Conference on Network Protocols, ICNP 2014
Y2 - 21 October 2014 through 24 October 2014
ER -
