Optimal Task Offloading and Resource Allocation in Mobile-Edge Computing With Inter-User Task Dependency

IEEE Transactions on Wireless Communications, pp. 235-250, 2020.

Cited by: 4|Bibtex|Views29|DOI:https://doi.org/10.1109/TWC.2019.2943563
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This paper has studied the impact of inter-user task dependency on the task offloading decisions and resource allocation in a two-user Mobile edge computing network

Abstract:

Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm to enhance the computing capability of hardware-constrained wireless devices (WDs). In this paper, we first consider a two-user MEC network, where each WD has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where t...More

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Introduction
  • The explosive growth of Internet of Things (IoT) in recent years enables cost-effective interconnections between tens of billions of wireless devices (WDs), such as sensors and wearable devices.
  • The key idea of MEC is to offload intensive computation tasks to the edges of radio access network, where much more powerful servers will compute on behalf of the resource-limited WDs. Compared with the traditional mobile cloud computing, MEC can overcome the drawbacks of high overhead and long backhaul latency.
  • Binary offloading requires each task to be either computed locally or offloaded to the MEC server as a whole.
  • On the other hand, allows a task to be partitioned and executed both locally and at the MEC server.
  • The authors consider binary computation offloading, which is commonly used in IoT systems for processing non-partitionable simple tasks [4], [5]
Highlights
  • The explosive growth of Internet of Things (IoT) in recent years enables cost-effective interconnections between tens of billions of wireless devices (WDs), such as sensors and wearable devices
  • We assume that the transmit power at the access point is fixed as 1 W and the peak transmit power of each wireless devices is 100 mW
  • This paper has studied the impact of inter-user task dependency on the task offloading decisions and resource allocation in a two-user Mobile edge computing network
  • We proposed efficient algorithms to optimize the resource allocation and task offloading decisions, with the goal of minimizing the weighted sum of the wireless devices’ energy consumption and task execution time
  • We assumed in this paper that each wireless devices is allocated with an orthogonal channel and the CPU frequency of the edge server is fixed
  • We can apply the deep reinforcement learning technique to quickly find a mapping between the timevarying channel gains and optimal offloading decisions
Results
  • The authors conduct numerical simulations to evaluate the performance of the optimal strategies.
  • Consider an example call graph in Fig. 6.
  • As for the computing workload, the authors assume that {Li,1} = [65.5 40.3 96.6] (Mcycles) Task in WD1 Task in WD2.
  • The edge server speed fc and the peak computational frequency of each WD fpeak are equal to 1010 and 108 cycles/s, respectively.
  • The authors consider a commercial mobile device in practice with the computing efficiency parameter κ = 10−26, which is consistent with the measurements in [29]
Conclusion
  • This paper has studied the impact of inter-user task dependency on the task offloading decisions and resource allocation in a two-user MEC network.
  • The authors assumed in this paper that each WD is allocated with an orthogonal channel and the CPU frequency of the edge server is fixed
  • The consideration of both bandwidth and computing resources competitions is needed when the authors extend the work to a large-size network.
  • The authors can apply the deep reinforcement learning technique to quickly find a mapping between the timevarying channel gains and optimal offloading decisions
Summary
  • Introduction:

    The explosive growth of Internet of Things (IoT) in recent years enables cost-effective interconnections between tens of billions of wireless devices (WDs), such as sensors and wearable devices.
  • The key idea of MEC is to offload intensive computation tasks to the edges of radio access network, where much more powerful servers will compute on behalf of the resource-limited WDs. Compared with the traditional mobile cloud computing, MEC can overcome the drawbacks of high overhead and long backhaul latency.
  • Binary offloading requires each task to be either computed locally or offloaded to the MEC server as a whole.
  • On the other hand, allows a task to be partitioned and executed both locally and at the MEC server.
  • The authors consider binary computation offloading, which is commonly used in IoT systems for processing non-partitionable simple tasks [4], [5]
  • Results:

    The authors conduct numerical simulations to evaluate the performance of the optimal strategies.
  • Consider an example call graph in Fig. 6.
  • As for the computing workload, the authors assume that {Li,1} = [65.5 40.3 96.6] (Mcycles) Task in WD1 Task in WD2.
  • The edge server speed fc and the peak computational frequency of each WD fpeak are equal to 1010 and 108 cycles/s, respectively.
  • The authors consider a commercial mobile device in practice with the computing efficiency parameter κ = 10−26, which is consistent with the measurements in [29]
  • Conclusion:

    This paper has studied the impact of inter-user task dependency on the task offloading decisions and resource allocation in a two-user MEC network.
  • The authors assumed in this paper that each WD is allocated with an orthogonal channel and the CPU frequency of the edge server is fixed
  • The consideration of both bandwidth and computing resources competitions is needed when the authors extend the work to a large-size network.
  • The authors can apply the deep reinforcement learning technique to quickly find a mapping between the timevarying channel gains and optimal offloading decisions
Tables
  • Table1: The number of searches performed by the one-climb based scheme and the brute-force method under different M and N
Download tables as Excel
Reference
  • J. Yan, S. Bi, and Y. J. Zhang, “Optimal offloading and resource allocation in mobile-edge computing with inter-user task dependency,” accepted by IEEE GLOBECOM, Dec. 2018.
    Google ScholarLocate open access versionFindings
  • Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2322–2358, Fourthquarter 2017.
    Google ScholarLocate open access versionFindings
  • W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, Oct. 2016.
    Google ScholarLocate open access versionFindings
  • L. Huang, S. Bi, and Y. J. Zhang, “Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks,” IEEE Trans. Mobile Comput., pp. 1–1, 2019.
    Google ScholarLocate open access versionFindings
  • S. Bi and Y. J. Zhang, “Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading,” IEEE Trans. Wireless Commun., vol. 17, no. 6, pp. 4177–4190, Jun. 2018.
    Google ScholarLocate open access versionFindings
  • F. Wang, J. Xu, X. Wang, and S. Cui, “Joint offloading and computing optimization in wireless powered mobile-edge computing systems,” IEEE Trans. Wireless Commun., vol. 17, no. 3, pp. 1784–1797, Mar. 2018.
    Google ScholarLocate open access versionFindings
  • C. You, K. Huang, and H. Chae, “Energy efficient mobile cloud computing powered by wireless energy transfer,” IEEE J. Sel. Areas Commun., vol. 34, no. 5, pp. 1757–1771, May 2016.
    Google ScholarLocate open access versionFindings
  • W. Zhang, Y. Wen, K. Guan, D. Kilper, H. Luo, and D. O. Wu, “Energyoptimal mobile cloud computing under stochastic wireless channel,” IEEE Trans. Wireless Commun., vol. 12, no. 9, pp. 4569–4581, Sept. 2013.
    Google ScholarLocate open access versionFindings
  • M. H. Chen, B. Liang, and M. Dong, “Joint offloading decision and resource allocation for multi-user multi-task mobile cloud,” in Proc. IEEE ICC, May 2016.
    Google ScholarLocate open access versionFindings
  • T. Q. Dinh, J. Tang, Q. D. La, and T. Q. S. Quek, “Offloading in mobile edge computing: Task allocation and computational frequency scaling,” IEEE Trans. Commun., vol. 65, no. 8, pp. 3571–3584, Aug. 2017.
    Google ScholarLocate open access versionFindings
  • H. Xing, L. Liu, J. Xu, and A. Nallanathan, “Joint task assignment and wireless resource allocation for cooperative mobile-edge computing,” in Proc. IEEE ICC, May 2018.
    Google ScholarLocate open access versionFindings
  • Y.-K. Kwok and I. Ahmad, “Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors,” IEEE Trans. Parallel Distrib. Syst., vol. 7, no. 5, pp. 506–521, May 1996.
    Google ScholarLocate open access versionFindings
  • M. A. Rodriguez and R. Buyya, “Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds,” IEEE Transactions on Cloud Computing, vol. 2, no. 2, pp. 222–235, Apr. 2014.
    Google ScholarLocate open access versionFindings
  • Z. Wu, Z. Ni, L. Gu, and X. Liu, “A revised discrete particle swarm optimization for cloud workflow scheduling,” in 2010 International Conference on Computational Intelligence and Security, Dec 2010, pp. 184–188.
    Google ScholarLocate open access versionFindings
  • S. Pandey, L. Wu, S. M. Guru, and R. Buyya, “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments,” in 2010 24th IEEE International Conference on Advanced Information Networking and Applications, April 2010, pp. 400–407.
    Google ScholarLocate open access versionFindings
  • S. B. P. D. Lorenzo and S. Sardellitti, “Joint optimization of radio resources and code partitioning in mobile edge computing,” submitted for publication, available on-line at http://arxiv.org/abs/1307.3835v3.
    Findings
  • W. Zhang, Y. Wen, and D. O. Wu, “Collaborative task execution in mobile cloud computing under a stochastic wireless channel,” IEEE Trans. Wireless Commun., vol. 14, no. 1, pp. 81–93, Jan. 2015.
    Google ScholarLocate open access versionFindings
  • W. Zhang and Y. Wen, “Energy-efficient task execution for application as a general topology in mobile cloud computing,” to appear in IEEE Transactions on Cloud Computing.
    Google ScholarFindings
  • M. Jia, J. Cao, and L. Yang, “Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing,” in Proc. IEEE INFOCOM WKSHPS, Apr. 2014.
    Google ScholarLocate open access versionFindings
  • S. Guo, B. Xiao, Y. Yang, and Y. Yang, “Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing,” in Proc. IEEE INFOCOM, Apr. 2016.
    Google ScholarLocate open access versionFindings
  • R. Viswanathan and P. K. Varshney, “Distributed detection with multiple sensors part i. fundamentals,” Proc. IEEE, vol. 85, no. 1, pp. 54–63, Jan 1997.
    Google ScholarLocate open access versionFindings
  • J. B. Predd, S. B. Kulkarni, and H. V. Poor, “Distributed learning in wireless sensor networks,” IEEE Signal Processing Magazine, vol. 23, no. 4, pp. 56–69, July 2006.
    Google ScholarLocate open access versionFindings
  • I. A. et al., “Wireless sensor networks: A survey,” Computer Networks, Elsevier Science, vol. 38, no. 4, pp. 393–422, 2002.
    Google ScholarLocate open access versionFindings
  • Y.. E. Wang, X. Lin, A. Adhikary, A. Grovlen, Y. Sui, Y. Blankenship, J. Bergman, and H. S. Razaghi, “A primer on 3gpp narrowband internet of things,” IEEE Communications Magazine, vol. 55, no. 3, pp. 117–123, March 2017.
    Google ScholarLocate open access versionFindings
  • S. Boyd and L. Vandenberghe, Convex Optimization. Cambidge University Press, 2004.
    Google ScholarFindings
  • S. Geman and D. Geman, “Stochastic relaxation, gibbs distributions, and the bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-6, no. 6, pp. 721–741, Nov 1984.
    Google ScholarLocate open access versionFindings
  • L. P. Qian, Y. J. A. Zhang, and M. Chiang, “Distributed nonconvex power control using gibbs sampling,” IEEE Trans. Commun., vol. 60, no. 12, pp. 3886–3898, Dec 2012.
    Google ScholarLocate open access versionFindings
  • D. Bertsimas and J. Tsitsiklis, “Simulated annealing,” Statistical Science, vol. 8, no. 1, pp. 10–15, 1993.
    Google ScholarLocate open access versionFindings
  • A. P. Miettinen and J. K. Nurminen, “Energy efficiency of mobile clients in cloud computing,” in Proc. 2nd USENIX Conf. Hot Topics Cloud Comput., Jun. 2010, pp. 4–11.
    Google ScholarLocate open access versionFindings
  • Y. Xiao, P. Savolainen, A. Karppanen, M. Siekkinen, and A. YlaJaaski, “Practical power modeling of data transmission over 802.11g for wireless applications,” in Proc. 1st Int. Conf. EnergyEfficient Comput. Netw., 2010, pp. 75–84.
    Google ScholarLocate open access versionFindings
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