TY - GEN
T1 - A cloud-based offloading service for computation-intensive mobile applications
AU - Huang, Bo Kai
AU - Cheng, Chih Chuan
AU - Lin, Chun Han
AU - Hsiu, Pi Cheng
N1 - Funding Information:
This work was supported in part by the Ministry of Science and Technology under Grant 102-2221-E-001-007-MY2 and by ITRI Cloud Computing Center for Mobile Applications.
Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - Mobile devices, which are inherently of limited computing capabilities, face a growing demand to support increasingly complex applications. Computation offloading addresses this issue by enabling mobile devices to offload computations to a remote server. Advancing on previous work, this paper presents a cloud-based offloading service, which models an optimization problem with the objective of minimizing the operation cost of the service provider while achieving the agreed quality of service (QoS) for subscribers. The problem is shown to be A/75-hard. We propose a pseudo-polynomial-time optimal algorithm for the offline scenario, as well as an efficient online algorithm that has a provable QoS guarantee and allows practical implementations. To evaluate our algorithms, we synthesize remotable tasks and conduct extensive simulations based on real mobile user traces and application workload patterns. Our results demonstrate that our online algorithm could achieve comparable performance to the optimal offline algorithm, in terms of both the required cloud cost and the provided user benefit.
AB - Mobile devices, which are inherently of limited computing capabilities, face a growing demand to support increasingly complex applications. Computation offloading addresses this issue by enabling mobile devices to offload computations to a remote server. Advancing on previous work, this paper presents a cloud-based offloading service, which models an optimization problem with the objective of minimizing the operation cost of the service provider while achieving the agreed quality of service (QoS) for subscribers. The problem is shown to be A/75-hard. We propose a pseudo-polynomial-time optimal algorithm for the offline scenario, as well as an efficient online algorithm that has a provable QoS guarantee and allows practical implementations. To evaluate our algorithms, we synthesize remotable tasks and conduct extensive simulations based on real mobile user traces and application workload patterns. Our results demonstrate that our online algorithm could achieve comparable performance to the optimal offline algorithm, in terms of both the required cloud cost and the provided user benefit.
KW - Cloud-based services
KW - Computation offloading
KW - Mobile applications
KW - Service cost optimization
UR - http://www.scopus.com/inward/record.url?scp=84962897435&partnerID=8YFLogxK
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U2 - 10.1109/RTCSA.2015.17
DO - 10.1109/RTCSA.2015.17
M3 - Conference contribution
AN - SCOPUS:84962897435
T3 - Proceedings - IEEE 21st International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2015
SP - 80
EP - 89
BT - Proceedings - IEEE 21st International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2015
A2 - O'Conner, Lisa
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE 21st International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2015
Y2 - 19 August 2015 through 21 August 2015
ER -