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OKRA: optimal task and resource allocation for energy minimization in mobile edge computing systems

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Abstract

To cope with the computational and energy constraints of mobile devices, Mobile Edge Computing (MEC) has recently emerged as a new paradigm that provides IT and cloud-computing services at mobile network edge in close proximity to mobile devices. This paper investigates the energy consumption problem for mobile devices in a multi-user MEC system with different types of computation tasks, random task arrivals, and unpredictable channel conditions. By jointly considering computation task scheduling, CPU frequency scaling, transmit power allocation and subcarrier bandwidth assignment, we formulate it as a stochastic optimization problem aiming at minimizing the power consumption of mobile devices and to maintain the long-term stability of task queues. By leveraging the Lyapunov optimization technique, we propose an online control algorithm (OKRA) to solve the formulation. We prove that this algorithm is able to provide deterministic worst-case latency guarantee for latency-sensitive computation tasks, and balance a desirable tradeoff between power consumption and system stability by appropriately tuning the control parameter. Extensive simulations are carried out to verify the theoretical analysis, and illustrate the impacts of critical parameters to algorithm performance.

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References

  1. Ahmed, E., & Rehmani, M. H. (2017). Mobile edge computing: Opportunities, solutions, and challenges. Future Generation Computer Systems, 70, 59–63. https://doi.org/10.1016/j.future.2016.09.015.

    Article  Google Scholar 

  2. Barbarossa, S., Sardellitti, S., & Lorenzo, P. D. (2013). Joint allocation of computation and communication resources in multiuser mobile cloud computing. In IEEE 14th workshop on signal processing advances in wireless communications (SPAWC) (pp. 26–30). https://doi.org/10.1109/SPAWC.2013.6612005.

  3. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. New York, NY: Cambridge University Press.

    Book  MATH  Google Scholar 

  4. Chen, X., Jiao, L., Li, W., & Fu, X. (2016). Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking, 24(5), 2795–2808. https://doi.org/10.1109/TNET.2015.2487344.

    Article  Google Scholar 

  5. Cisco. (2017). Cisco Visual Networking Index: Global mobile data traffic forecast update, 2016–2021 Whitepaper.

  6. Dinh, T. Q., Tang, J., La, Q. D., & Quek, T. Q. S. (2017). Adaptive computation scaling and task offloading in mobile edge computing. In IEEE wireless communications and networking conference (WCNC) (pp. 1–6). https://doi.org/10.1109/WCNC.2017.7925612.

  7. Fan, Q., & Ansari, N. (2018). Application aware workload allocation for edge computing-based iot. IEEE Internet of Things Journal, 5(3), 2146–2153. https://doi.org/10.1109/JIOT.2018.2826006.

    Article  Google Scholar 

  8. Fan, Q., & Ansari, N. (2018). Towards workload balancing in fog computing empowered IoT. IEEE Transactions on Network Science and Engineering,. https://doi.org/10.1109/TNSE.2018.2852762.

    Google Scholar 

  9. Fan, Q., Ansari, N., & Sun, X. (2017). Energy driven avatar migration in green cloudlet networks. IEEE Communications Letters, 21(7), 1601–1604. https://doi.org/10.1109/LCOMM.2017.2684812.

    Article  Google Scholar 

  10. Fang, W., An, Z., Shu, L., Liu, Q., Xu, Y., & An, Y. (2014). Achieving optimal admission control with dynamic scheduling in energy constrained network systems. Journal of Network and Computer Applications, 44, 152–160. https://doi.org/10.1016/j.jnca.2014.05.009.

    Article  Google Scholar 

  11. Fang, W., Li, Y., Zhang, H., Xiong, N., Lai, J., & Vasilakos, A. V. (2014). On the throughput-energy tradeoff for data transmission between cloud and mobile devices. Information Sciences, 283, 79–93. https://doi.org/10.1016/j.ins.2014.06.022. (New trend of computational intelligence in human–robot interaction).

    Article  Google Scholar 

  12. Huang, J., Qian, F., Gerber, A., Mao, Z. M., Sen, S., & Spatscheck, O. (2012). A close examination of performance and power characteristics of 4G LTE networks. In Proceedings of the 10th international conference on mobile systems, applications, and services, MobiSys ’12 (pp. 225–238). New York, NY: ACM. https://doi.org/10.1145/2307636.2307658.

  13. Jeong, S., Simeone, O., & Kang, J. (2017). Mobile edge computing via a UAV-mounted cloudlet: Optimization of bit allocation and path planning. IEEE Transactions on Vehicular Technology,. https://doi.org/10.1109/TVT.2017.2706308.

    Google Scholar 

  14. Kumar, K., Liu, J., Lu, Y. H., & Bhargava, B. (2013). A survey of computation offloading for mobile systems. Mobile Networks and Applications, 18(1), 129–140. https://doi.org/10.1007/s11036-012-0368-0.

    Article  Google Scholar 

  15. Kwak, J., Choi, O., Chong, S., & Mohapatra, P. (2014). Dynamic speed scaling for energy minimization in delay-tolerant smartphone applications. In IEEE conference on computer communications, IEEE INFOCOM (pp. 2292–2300). https://doi.org/10.1109/INFOCOM.2014.6848173.

  16. Kwak, J., Kim, Y., Lee, J., & Chong, S. (2015). Dream: Dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE Journal on Selected Areas in Communications, 33(12), 2510–2523. https://doi.org/10.1109/JSAC.2015.2478718.

    Article  Google Scholar 

  17. Li, A., Yang, X., Kandula, S., & Zhang, M. (2010). Cloudcmp: Comparing public cloud providers. In Proceedings of the 10th ACM SIGCOMM conference on internet measurement, IMC ’10 (pp. 1–14). New York, NY: ACM. https://doi.org/10.1145/1879141.1879143.

  18. Li, S., Zhou, Y., Jiao, L., Yan, X., Wang, X., & Lyu, M. R. T. (2015). Towards operational cost minimization in hybrid clouds for dynamic resource provisioning with delay-aware optimization. IEEE Transactions on Services Computing, 8(3), 398–409. https://doi.org/10.1109/TSC.2015.2390413.

    Article  Google Scholar 

  19. Li, Y., Shi, Y., Sheng, M., Wang, C. X., Li, J., Wang, X., et al. (2016). Energy-efficient transmission in heterogeneous wireless networks: A delay-aware approach. IEEE Transactions on Vehicular Technology, 65(9), 7488–7500. https://doi.org/10.1109/TVT.2015.2472578.

    Article  Google Scholar 

  20. Liu, F., Shu, P., Jin, H., Ding, L., Yu, J., Niu, D., et al. (2013). Gearing resource-poor mobile devices with powerful clouds: Architectures, challenges, and applications. IEEE Wireless Communications, 20(3), 14–22. https://doi.org/10.1109/MWC.2013.6549279.

    Article  Google Scholar 

  21. Liu, J., Mao, Y., Zhang, J., & Letaief, K. B. (2016). Delay-optimal computation task scheduling for mobile-edge computing systems. In IEEE international symposium on information theory (ISIT) (pp. 1451–1455). https://doi.org/10.1109/ISIT.2016.7541539.

  22. Liu, L., Chang, Z., & Guo, X. (2018). Socially aware dynamic computation offloading scheme for fog computing system with energy harvesting devices. IEEE Internet of Things Journal, 5(3), 1869–1879. https://doi.org/10.1109/JIOT.2018.2816682.

    Article  Google Scholar 

  23. Liu, L., Chang, Z., Guo, X., Mao, S., & Ristaniemi, T. (2018). Multiobjective optimization for computation offloading in fog computing. IEEE Internet of Things Journal, 5(1), 283–294. https://doi.org/10.1109/JIOT.2017.2780236.

    Article  Google Scholar 

  24. Liu, L., Guo, X., Chang, Z., & Ristaniemi, T. (2019). Joint optimization of energy and delay for computation offloading in cloudlet-assisted mobile cloud computing. Wireless Networks, 25(4), 2027–2040.

    Article  Google Scholar 

  25. Ma, X., Zhao, Y., Zhang, L., Wang, H., & Peng, L. (2013). When mobile terminals meet the cloud: Computation offloading as the bridge. IEEE Network, 27(5), 28–33. https://doi.org/10.1109/MNET.2013.6616112.

    Article  Google Scholar 

  26. Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys Tutorials,. https://doi.org/10.1109/COMST.2017.2682318.

    Google Scholar 

  27. Mao, Y., Zhang, J., & Letaief, K. B. (2016). Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE Journal on Selected Areas in Communications, 34(12), 3590–3605. https://doi.org/10.1109/JSAC.2016.2611964.

    Article  Google Scholar 

  28. Mao, Y., Zhang, J., Song, S. H., & Letaief, K. B. (2016). Power-delay tradeoff in multi-user mobile-edge computing systems. In IEEE global communications conference (GLOBECOM) (pp. 1–6). https://doi.org/10.1109/GLOCOM.2016.7842160.

  29. Neely, M. J. (2010). Stochasitic network optimization with application to communication and queueing systems. San Rafael: Morgan & Claypool.

    MATH  Google Scholar 

  30. Neely, M. J. (2011). Opportunistic scheduling with worst case delay guarantees in single and multi-hop networks. In Proceedings IEEE INFOCOM (pp. 1728–1736). https://doi.org/10.1109/INFCOM.2011.5934971.

  31. Patel, M., Naughton, B., Chan, C., Sprecher, N., Abeta, S., Neal, A., et al. (2014). Mobile-edge computing introductory technical white paper, White paper, Mobile-edge computing (MEC) industry initiative.

  32. Samanta, A., & Chang, Z. (2019). Adaptive service offloading for revenue maximization in mobile edge computing with delay-constraint. IEEE Internet of Things Journal,. https://doi.org/10.1109/JIOT.2019.2892398.

    Google Scholar 

  33. Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39. https://doi.org/10.1109/MC.2017.9.

    Article  Google Scholar 

  34. Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4), 14–23. https://doi.org/10.1109/MPRV.2009.82.

    Article  Google Scholar 

  35. Shi, C., Habak, K., Pandurangan, P., Ammar, M., Naik, M., & Zegura, E. (2014). Cosmos: Computation offloading as a service for mobile devices. In Proceedings of the 15th ACM international symposium on mobile ad hoc networking and computing, MobiHoc ’14 (pp. 287–296). New York, NY: ACM. https://doi.org/10.1145/2632951.2632958.

  36. Sun, X., Ansari, N., & Fan, Q. (2015). Green energy aware avatar migration strategy in green cloudlet networks. In IEEE 7th international conference on cloud computing technology and science (CloudCom) (pp. 139–146). https://doi.org/10.1109/CloudCom.2015.23.

  37. Tran, T. X., Pandey, P., Hajisami, A., & Pompili, D. (2017). Collaborative multi-bitrate video caching and processing in mobile-edge computing networks. In 13th Annual conference on wireless on-demand network systems and services (WONS) (pp. 165–172). https://doi.org/10.1109/WONS.2017.7888772.

  38. Urgaonkar, R., Urgaonkar, B., Neely, M. J., & Sivasubramaniam, A. (2011). Optimal power cost management using stored energy in data centers. In Proceedings of the ACM SIGMETRICS joint international conference on measurement and modeling of computer systems, SIGMETRICS ’11 (pp. 221–232). New York, NY: ACM. https://doi.org/10.1145/1993744.1993766.

  39. Wang, Y., Sheng, M., Wang, X., Wang, L., & Li, J. (2016). Mobile-edge computing: Partial computation offloading using dynamic voltage scaling. IEEE Transactions on Communications, 64(10), 4268–4282. https://doi.org/10.1109/TCOMM.2016.2599530.

    Google Scholar 

  40. Wu, H., Knottenbelt, W., Wolter, K., & Sun, Y. (2016). An optimal offloading partitioning algorithm in mobile cloud computing (pp. 311–328). Cham: Springer. https://doi.org/10.1007/978-3-319-43425-4_21.

    Google Scholar 

  41. You, C., Huang, K., Chae, H., & Kim, B. H. (2017). Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Transactions on Wireless Communications, 16(3), 1397–1411. https://doi.org/10.1109/TWC.2016.2633522.

    Article  Google Scholar 

  42. Yu, Y., Zhang, J., & Letaief, K. B. (2016). Joint subcarrier and cpu time allocation for mobile edge computing. In IEEE global communications conference (GLOBECOM) (pp. 1–6). https://doi.org/10.1109/GLOCOM.2016.7841937.

  43. Zhang, K., Mao, Y., Leng, S., Zhao, Q., Li, L., Peng, X., et al. (2016). Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access, 4, 5896–5907. https://doi.org/10.1109/ACCESS.2016.2597169.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities of China under Grants 2019JBM027.

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

    Weiwei Fang, Shuai Ding & Wenchen Zhou

  2. National Engineering Laboratory for Public Safety Risk Perception and Control, China Academy of Electronics and Information Technology, Beijing, China

    Yangyang Li

  3. Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, USA

    Naixue Xiong

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Fang, W., Ding, S., Li, Y. et al. OKRA: optimal task and resource allocation for energy minimization in mobile edge computing systems. Wireless Netw 25, 2851–2867 (2019). https://doi.org/10.1007/s11276-019-02000-y

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