TY - GEN
T1 - Energy-efficient computation offloading in cellular networks
AU - Geng, Yeli
AU - Hu, Wenjie
AU - Yang, Yi
AU - Gao, Wei
AU - Cao, Guohong
N1 - Funding Information:
This work was supported in part by the National Science Foundation (NSF) under grant CNS- 1218597, CNS-1526425, and CNS-1421578.
Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2016/3/18
Y1 - 2016/3/18
N2 - Computationally intensive applications may quickly drain mobile device batteries. One viable solution to address this problem utilizes computation offloading. The tradeoff is that computation offloading introduces additional communication, with a corresponding energy cost. Yet, previous research into computation offloading has failed to account for the special characteristics of cellular networks that impact mobile device energy consumption. In this paper, we aim to develop energy efficient computation offloading algorithms for cellular networks. We analyze the effects of the long tail problem on task offloading, formalize the computation offloading problem, and use Dijkstra's algorithm to find the optimal decision. Since this optimal solution relies on perfect knowledge of future tasks, we further propose an online algorithm for offloading. We have implemented this latter algorithm on Android-based smartphones. Both experimental results from this implementation and trace-driven simulation show that our algorithm can significantly reduce the energy of computation offloading in cellular networks.
AB - Computationally intensive applications may quickly drain mobile device batteries. One viable solution to address this problem utilizes computation offloading. The tradeoff is that computation offloading introduces additional communication, with a corresponding energy cost. Yet, previous research into computation offloading has failed to account for the special characteristics of cellular networks that impact mobile device energy consumption. In this paper, we aim to develop energy efficient computation offloading algorithms for cellular networks. We analyze the effects of the long tail problem on task offloading, formalize the computation offloading problem, and use Dijkstra's algorithm to find the optimal decision. Since this optimal solution relies on perfect knowledge of future tasks, we further propose an online algorithm for offloading. We have implemented this latter algorithm on Android-based smartphones. Both experimental results from this implementation and trace-driven simulation show that our algorithm can significantly reduce the energy of computation offloading in cellular networks.
UR - http://www.scopus.com/inward/record.url?scp=84969786840&partnerID=8YFLogxK
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U2 - 10.1109/ICNP.2015.20
DO - 10.1109/ICNP.2015.20
M3 - Conference contribution
AN - SCOPUS:84969786840
T3 - Proceedings - International Conference on Network Protocols, ICNP
SP - 145
EP - 155
BT - Proceedings - 2015 IEEE 23rd International Conference on Network Protocols, ICNP 2015
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Network Protocols, ICNP 2015
Y2 - 10 November 2015 through 13 November 2015
ER -
