Online task offloading in non-stationary fog-enabled networks

doi:10.7523/j.issn.2095-6134.2020.05.015

›› 2020, Vol. 37 ›› Issue (5): 699-707.DOI: 10.7523/j.issn.2095-6134.2020.05.015

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Online task offloading in non-stationary fog-enabled networks

ZHU Zhaowei^1,2,3, LIU Ting³, QIAN Hua⁴, LUO Xiliang³

1. Shanghai Institute of Microsystem&Information Technology, Chinese Academy of Sciences, Shanghai 200050, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. ShanghaiTech University, Shanghai 201210, China;
4. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China

Received:2019-02-15 Revised:2019-03-28 Online:2020-09-15
Supported by:

Abstract

Abstract: To fully exploit the computational resources in different fog nodes, task offloading is emerging. In this work, under the fog computing scenario, an efficient online task offloading strategy is investigated to minimize the long-term cost of task offloading. To achieve this goal, the problem is modeled as a stochastic optimization problem. Moreover, the system parameters are characterized by random variables, and their expectations may change abruptly at unknown time slot. Besides, the information about the system parameters is only available through the feedbacks after the task finishes. Using the non-stationary multi-armed bandit framework, we propose an efficient algorithm to handle this challenging stochastic programming. Furthermore, theoretical analyses are presented to prove the asymptotic optimality of the proposed algorithm. Numerical results reveal the advantages of this algorithm.

Key words: online learning, fog computing, task offloading, stochastic optimization, multi-armed bandit

CLC Number:

TP393

ZHU Zhaowei, LIU Ting, QIAN Hua, LUO Xiliang. Online task offloading in non-stationary fog-enabled networks[J]. , 2020, 37(5): 699-707.

References

[1] 王雷,王平建,向继. 云存储环境中的统一认证技术[J]. 中国科学院大学学报,2015,32(5):682-688.
[2] Dinh T Q, Tang J, La Q D, et al. Offloading in mobile edge computing:task allocation and computational frequency scaling[J]. IEEE Transaction on Communication, 2017, 65(8):3571-3584.
[3] You C, Huang K, Chae H, et al. Energy-efficient resource allocation for mobile-edge computation offloading[J]. IEEE Transaction on Wireless Communication, 2017, 16(3):1397-1411.
[4] Kwak J, Kim Y, Lee J, et al. DREAM:Dynamic resource and task allocation for energy minimization in mobile cloud systems[J]. IEEE Journal on Selected Areas of Communication, 2015, 33(12):2510-2523.
[5] Mao Y, Zhang J, Song S H, et al. Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems[J]. IEEE Transaction on Wireless Communication, 2017, 16(9):5994-6009.
[6] Pu L, Chen X, Xu J, et al. D2D fogging:an energy-efficient and incentive-aware task offloading framework via network-assisted D2D collaboration[J]. IEEE Journal on Selected Areas of Communication, 2016, 34(12):3887-3901.
[7] Yang Y, Zhao S, Zhang W, et al. DEBTS:delay energy balanced task scheduling in homogeneous fog networks[J]. IEEE Internet Things Journal, 2018, 5(3):2094-2106.
[8] Chen X. Decentralized computation offloading game for mobile cloud computing[J]. IEEE Transaction on Parallel and Distributed Systems, 2015, 26(4):974-983.
[9] Chen T, Giannakis G B. Bandit convex optimization for scalable and dynamic IoT management[J]. IEEE Internet Things Journal, 2019, 6(1):1276-1286.
[10] Tekin C, Van Der Schaar M. An experts learning approach to mobile service offloading[C]//The IEEE Annual Allerton Conference on Communication, Control, and Computing. 2014:643-650.
[11] Auer P, Cesa-Bianchi N, Fischer P. Finite-time analysis of the multiarmed bandit problem[J]. Machine Learning, 2002, 47(2):235-256.
[12] Bubeck S, Cesa-Bianchi N. Regret analysis of stochastic and nonstochastic multi-armed bandit problems[J] Foundations and Trends in Machine Learning, 2012, 5(1):1-122.
[13] Berry D A, Fristedt B. Bandit problems:sequential allocation of experiments[M]. London, U.K.:Chapman & Hall, 1985.
[14] Garivier A, Moulines E. On upper-confidence bound policies for switching bandit problems[C]//The 2011 International Conference on Algorithmic Learning Theory, 2011:174-188.
[15] Joulani P, Gyorgy A, Szepesvari C. Online learning under delayed feedback[C]//The 30th International Conference on Machine Learning. 2013:1453-1461.
[16] Zhu Z, Liu T, Jin S, et al. Learn and pick right nodes to offload[EB/OL].(2018-04-24)[2019-02-10].https://arxiv.org/pdf/1804.08416.pdf.

Online task offloading in non-stationary fog-enabled networks

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