Abstract
Edge computing could play an important role in Internet of Things (IoT). Computing capacity allocation has been researched a lot in mobile edge computing, which is task oriented. However, hierarchical edge computing also needs computing capacity allocation which is node oriented. This paper focusses on capacity allocation of nodes in hierarchical edge computing. We take energy efficiency and loss in high concurrency scenarios into consideration and work out a method to do allocation by weighing loss and energy efficiency. Simulation is under circumstances that nodes overload, which means that loss is inevitable. A new inspiration of deployment is also given after simulation.
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References
Kiani, A., Ansari, N., Khreishah, A.: Hierarchical capacity provisioning for fog computing. IEEE/ACM Trans. Netw. 27(3), 962–971 (2019)
Li, Y., Wang, S.: An energy-aware edge server placement algorithm in mobile edge computing. In: 2018 IEEE International Conference on Edge Computing (EDGE), San Francisco, CA, pp. 66–73 (2018)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN 1995), pp. 1942–1948 (1995).
Laskari, E.C., Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization for integer programming. In: Proceedings of the Congress on Evolutionary Computation (CEC 2002), pp. 1582–1587 (2002)
Liu, Z., Peng, T., Peng, B., Wang, W.: Sum-capacity of D2D and cellular hybrid networks over cooperation and non-cooperation. In: Proceedings of 7th International ICST Conference on Communications and Networking, China, pp. 707–711 (2012)
Yuan, P., Cai, Y., Huang, X., Tang, S., Zhao, X.: Collaboration improves the capacity of mobile edge computing. IEEE Internet Things J. 6(6), 10610–10619 (2019)
Lin, Y., Lai, Y., Huang, J., Chien, H.: Three-tier capacity and traffic allocation for core, edges, and devices for mobile edge computing. IEEE Trans. Netw. Serv. Manag. 15(3), 923–933 (2018)
Noreikis, M., Xiao, Y., Ylä-Jaäiski, A.: QoS-oriented capacity planning for edge computing. In: 2017 IEEE International Conference on Communications (ICC), Paris, pp. 1–6 (2017)
H. Badri, T. Bahreini, D. Grosu and K. Yang: Risk-Based Optimization of Resource Provisioning in Mobile Edge Computing. In: 2018 IEEE/ACM Symposium on Edge Computing (SEC), Seattle, WA, pp. 328–330. (2018).
Liu, M., Liu, Y.: Price-based distributed offloading for mobile-edge computing with computation capacity constraints. IEEE Wirel. Commun. Lett. 7(3), 420–423 (2018)
Dayarathna, M., Wen, Y.G., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18(1), 732–794 (2016)
Wang, S., Liu, Z., Zheng, Z., Sun, Q., Yang, F.: Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers. In: Proceedings of the 19th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2013), pp. 102–109 (2013)
Texas Instruments: CMOS Power Consumption and Cpd Calculation. SCAA.35B (1997)
Acknowledgements
This work was supported by the National Key Research and Development Program of China (grant number 2018YFC0831304).
The National Natural Science Foundation of China under Grant 61701019.
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Zhou, Z., Zhang, Z., Zeng, J., Li, J. (2021). Computing Capacity Allocation for Hierarchical Edge Computing Nodes in High Concurrency Scenarios Based on Energy Efficiency Evaluation. In: Lin, YB., Deng, DJ. (eds) Smart Grid and Internet of Things. SGIoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-030-69514-9_20
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DOI: https://doi.org/10.1007/978-3-030-69514-9_20
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