TY - GEN
T1 - Energy-efficient scheduling policy for collaborative execution in mobile cloud computing
AU - Zhang, Weiwen
AU - Wen, Yonggang
AU - Wu, Dapeng Oliver
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2013
Y1 - 2013
N2 - In this paper, we investigate the scheduling policy for collaborative execution in mobile cloud computing. A mobile application is represented by a sequence of fine-grained tasks formulating a linear topology, and each of them is executed either on the mobile device or offloaded onto the cloud side for execution. The design objective is to minimize the energy consumed by the mobile device, while meeting a time deadline. We formulate this minimum-energy task scheduling problem as a constrained shortest path problem on a directed acyclic graph, and adapt the canonical 'LARAC' algorithm to solving this problem approximately. Numerical simulation suggests that a one-climb offloading policy is energy efficient for the Markovian stochastic channel, in which at most one migration from mobile device to the cloud is taken place for the collaborative task execution. Moreover, compared to standalone mobile execution and cloud execution, the optimal collaborative execution strategy can significantly save the energy consumed on the mobile device. © 2013 IEEE.
AB - In this paper, we investigate the scheduling policy for collaborative execution in mobile cloud computing. A mobile application is represented by a sequence of fine-grained tasks formulating a linear topology, and each of them is executed either on the mobile device or offloaded onto the cloud side for execution. The design objective is to minimize the energy consumed by the mobile device, while meeting a time deadline. We formulate this minimum-energy task scheduling problem as a constrained shortest path problem on a directed acyclic graph, and adapt the canonical 'LARAC' algorithm to solving this problem approximately. Numerical simulation suggests that a one-climb offloading policy is energy efficient for the Markovian stochastic channel, in which at most one migration from mobile device to the cloud is taken place for the collaborative task execution. Moreover, compared to standalone mobile execution and cloud execution, the optimal collaborative execution strategy can significantly save the energy consumed on the mobile device. © 2013 IEEE.
KW - collaborative execution
KW - mobile cloud computing
KW - scheduling policy
UR - http://www.scopus.com/inward/record.url?scp=84883123478&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84883123478&origin=recordpage
U2 - 10.1109/INFCOM.2013.6566761
DO - 10.1109/INFCOM.2013.6566761
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781467359467
T3 - Proceedings - IEEE INFOCOM
SP - 190
EP - 194
BT - 2013 Proceedings IEEE INFOCOM 2013
T2 - 32nd IEEE Conference on Computer Communications (IEEE INFOCOM 2013)
Y2 - 14 April 2013 through 19 April 2013
ER -