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Cluster-PSO Based Resource Orchestration for Multi-task Applications in Vehicular Cloud

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Abstract

Vehicular cloud computing (VCC) provides a vehicular user attaching several resources with different types at the same time. Additionally, the vehicular applications especially for big data processing are always complicated and may be decomposed into several fine-grained tasks. When offloading the complicated multi-task application to the vehicular clouds, the task executes individually in terms of its own computation, storage and bandwidth requirement. Different from the task offloading in mobile cloud computing which aims to optimize the energy consumption, the important metric for vehicular users is the application delay. Moreover, the moving vehicles always have the similar resource properties and may form the solution clusters when finding the resource orchestration policy, which brings an opportunity of improving resource orchestration performance. In this paper, we formulate the VCC resource orchestration as an optimization problem, and propose a cluster-particle swarm optimization (PSO) algorithm to obtain the resource orchestration policy. A fast cluster algorithm is used to divide the solution space and generate sub-swarms for better exploring the orchestration solutions. The experiment results show that the cluster-PSO algorithm can achieve a higher resource orchestration accuracy in an acceptable time comparing to the other PSO algorithms. Especially, when there are more tasks in an application and the vehicle has more optional VCC resources, the performance of the cluster-PSO based resource orchestration is outstanding.

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Acknowledgements

This research was jointly supported by: (1) National Natural Science Foundation of China (Nos. 61471063, 61671079, 61771068, 61421061, 61302087); (2) Key (Keygrant) Project of Chinese Ministry of Education (No. MCM20130310); (3) Beijing Municipal Natural Science Foundation (No. 4152039).

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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, People’s Republic of China

    Qi Qi, Jingyu Wang, Jing Wang, Haifeng Sun & Jianxin Liao

  2. EBUPT Information Technology Co., Ltd., Beijing, 100191, People’s Republic of China

    Yufei Cao

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  1. Qi Qi
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  2. Jingyu Wang
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  3. Yufei Cao
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Correspondence to Qi Qi.

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Qi, Q., Wang, J., Cao, Y. et al. Cluster-PSO Based Resource Orchestration for Multi-task Applications in Vehicular Cloud. Wireless Pers Commun 102, 2133–2155 (2018). https://doi.org/10.1007/s11277-017-5046-x

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  • DOI: https://doi.org/10.1007/s11277-017-5046-x

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