TY - GEN
T1 - Cooperative Offloading Based on Online Auction for Mobile Edge Computing
AU - Zheng, Xiao
AU - Shah, Syed Bilal Hussain
AU - Nawaf, Liqaa
AU - Rana, Omer F.
AU - Zhu, Yuanyuan
AU - Gan, Jianyuan
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/11/17
Y1 - 2022/11/17
N2 - In the field of edge computing, collaborative computing offloading, in which edge users offload tasks to adjacent mobile devices with rich resources in an opportunistic manner, provides a promising example to meet the requirements of low latency. However, most of the previous work has been based on the assumption that these mobile devices are willing to serve edge users, with no incentive strategy. In this paper, an online auction-based strategy is proposed, in which both users and mobile devices can interact dynamically with the system. The auction strategy proposed in this paper is based on an online approach to optimize the long-term utility of the system, such as start time, length and size, resource requirements, and evaluation valuation, without knowing the future. Experiments verify that the proposed online auction strategy achieves the expected attributes such as individual rationality, authenticity and computational ease of handling. In addition, the index of theoretical competitive ratio also indicates that the proposed online mechanism realizes near-offline optimal long-term utility performance.
AB - In the field of edge computing, collaborative computing offloading, in which edge users offload tasks to adjacent mobile devices with rich resources in an opportunistic manner, provides a promising example to meet the requirements of low latency. However, most of the previous work has been based on the assumption that these mobile devices are willing to serve edge users, with no incentive strategy. In this paper, an online auction-based strategy is proposed, in which both users and mobile devices can interact dynamically with the system. The auction strategy proposed in this paper is based on an online approach to optimize the long-term utility of the system, such as start time, length and size, resource requirements, and evaluation valuation, without knowing the future. Experiments verify that the proposed online auction strategy achieves the expected attributes such as individual rationality, authenticity and computational ease of handling. In addition, the index of theoretical competitive ratio also indicates that the proposed online mechanism realizes near-offline optimal long-term utility performance.
KW - Collaborative computing offloading
KW - Long-term utility
KW - Online auction strategy
UR - http://www.scopus.com/inward/record.url?scp=85142900393&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-19211-1_51
DO - 10.1007/978-3-031-19211-1_51
M3 - Conference contribution
AN - SCOPUS:85142900393
SN - 9783031192104
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 617
EP - 628
BT - Wireless Algorithms, Systems, and Applications - 17th International Conference, WASA 2022, Proceedings
A2 - Wang, Lei
A2 - Segal, Michael
A2 - Chen, Jenhui
A2 - Qiu, Tie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2022
Y2 - 24 November 2022 through 26 November 2022
ER -