SCIENCE CHINA Information Sciences, Volume 64 , Issue 8 : 182302(2021) https://doi.org/10.1007/s11432-020-3094-6

Energy-efficient URLLC service provisioning in softwarization-based networks

More info
  • ReceivedMay 23, 2020
  • AcceptedOct 20, 2020
  • PublishedJul 9, 2021



This work was supported by National Natural Science Foundation of China (Grant Nos. 61871099, 61631004) and China Postdoctoral Science Foundation (Grant No. 2019M663476). We gratefully acknowledge the many helpful suggestions made by Shaoe LIN, Qihao LI, Weizhang TING, Junlin LI, Nan CHEN, and anonymous referees. We also thanks to the support of joint training public postgraduates of Chinese Scholarship Council (CSC).


[1] Series M. Framework and overall objectives of the future development of imt for 2020 and beyond. Recommendation ITU-2083, 2015. Google Scholar

[2] Ye Q, Li J, Qu K. End-to-End Quality of Service in 5G Networks: Examining the Effectiveness of a Network Slicing Framework. IEEE Veh Technol Mag, 2018, 13: 65-74 CrossRef Google Scholar

[3] Ye Q, Zhuang W, Li X. End-to-End Delay Modeling for Embedded VNF Chains in 5G Core Networks. IEEE Internet Things J, 2019, 6: 692-704 CrossRef Google Scholar

[4] Zhang S, Wu Q, Xu S. Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks. IEEE Commun Surv Tutorials, 2017, 19: 33-56 CrossRef Google Scholar

[5] Mukherjee A. Energy Efficiency and Delay in 5G Ultra-Reliable Low-Latency Communications System Architectures. IEEE Network, 2018, 32: 55-61 CrossRef Google Scholar

[6] Dharmaweera M N, Parthiban R, Sekercioglu Y A. Toward a Power-Efficient Backbone Network: The State of Research. IEEE Commun Surv Tutorials, 2015, 17: 198-227 CrossRef Google Scholar

[7] Chen Y, Zhang N, Zhang Y. Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things. IEEE Trans Cloud Comput, 2019, : 1-1 CrossRef Google Scholar

[8] Fei X C, Liu F M, Xu H, et al . Adaptive VNF scaling and flow routing with proactive demand prediction. In: Proceedings of IEEE INFOCOM, Honolulu, 2018. 486--494. Google Scholar

[9] Yu B, Han Y N, Wen X M, et al. An energy-aware algorithm for optimizing resource allocation in software defined network. In: Proceedings of IEEE GLOBECOM, Washington, DC USA, 2016. 1--7. Google Scholar

[10] Liu M, Feng G, Zhou J. Joint Two-Tier Network Function Parallelization on Multicore Platform. IEEE Trans Netw Serv Manage, 2019, 16: 990-1004 CrossRef Google Scholar

[11] Floyd S, Jacobson V. Random early detection gateways for congestion avoidance. IEEE/ACM Trans Networking, 1993, 1: 397-413 CrossRef Google Scholar

[12] Bolot J-C. End-to-end packet delay and loss behavior in the Internet. In: Proceedings of ACM SIGCOMM Computer Communication Review, 1993. 289--298. Google Scholar

[13] Beck M T, Botero J F. Coordinated allocation of service function chains. In: Proceedings of 2015 IEEE Global Communications Conference (GLOBECOM), 2015. 1--6. Google Scholar

[14] Zhang Q X, Xiao Y K, Liu F M, et al. Joint optimization of chain placement and request scheduling for network function virtualization. In: Proceedings of IEEE ICDCS, 2017. 731--741. Google Scholar

[15] Nguyen D T, Le L B, Bhargava V K. A Market-Based Framework for Multi-Resource Allocation in Fog Computing. IEEE/ACM Trans Networking, 2019, 27: 1151-1164 CrossRef Google Scholar

[16] Zhou Z, Dong M, Ota K. Energy-Efficient Resource Allocation for D2D Communications Underlaying Cloud-RAN-Based LTE-A Networks. IEEE Internet Things J, 2016, 3: 428-438 CrossRef Google Scholar

[17] Lyu X, Tian H, Ni W. Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing. IEEE Trans Commun, 2018, 66: 2603-2616 CrossRef Google Scholar

[18] Han Z H, Tan H S, Chen G H, et al. Dynamic virtual machine management via approximate markov decision process. In: Proceedings of IEEE INFOCOM , San Francisco, CA, USA, 2016. 1--9. Google Scholar

[19] Chen L, Zhou S, Xu J. Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks. IEEE/ACM Trans Networking, 2018, 26: 1619-1632 CrossRef Google Scholar

[20] Ye Q, Li J, Qu K. End-to-End Quality of Service in 5G Networks: Examining the Effectiveness of a Network Slicing Framework. IEEE Veh Technol Mag, 2018, 13: 65-74 CrossRef Google Scholar

[21] Sun C, Bi J, Zheng Z L, et al. NFP: Enabling network function parallelism in NFV. In: Proceedings of ACM Conference of Special Interest Group on Data Communication, 2017. 43--56. Google Scholar

[22] Chen L H, Shen H Y. Consolidating complementary VMs with spatial/temporal-awareness in cloud datacenters. In: Proceedings of IEEE INFOCOM, 2014. 1033--1041. Google Scholar

[23] Zhang Q, Zhani M F, Boutaba R. Dynamic Heterogeneity-Aware Resource Provisioning in the Cloud. IEEE Trans Cloud Comput, 2014, 2: 14-28 CrossRef Google Scholar

[24] Reiss C, Wilkes J, Hellerstein J L. Google cluster-usage traces: format+ schema. 2011. Google Scholar

[25] Burke P J. The Output of a Queuing System. Operations Res, 1956, 4: 699-704 CrossRef Google Scholar

[26] Abu Sharkh M, Jammal M, Shami A. Resource allocation in a network-based cloud computing environment: design challenges. IEEE Commun Mag, 2013, 51: 46-52 CrossRef Google Scholar

[27] Sun X H and Ni L M. Another view on parallel speedup. In: Proceedings of IEEE/ACM Conference on Supercomputing, 1990. 324--333. Google Scholar

[28] Mahadevan P, Sharma P, Banerjee S, et al. A power benchmarking framework for network devices. In: Proceedings of International Conference on Research in Networking. Springer, 2009. 795--808. Google Scholar

[29] Liu H K, Xu C-Z, Jin H, et al. Performance and energy modeling for live migration of virtual machines. In: Proceedings of ACM International Symposium on High Performance Distributed Computing, 2011. 171--182. Google Scholar

[30] Wen R, Feng G, Tang J. On Robustness of Network Slicing for Next-Generation Mobile Networks. IEEE Trans Commun, 2019, 67: 430-444 CrossRef Google Scholar

[31] Korf R E. Depth-first iterative-deepening. Artificial Intelligence, 1985, 27: 97-109 CrossRef Google Scholar

[32] Myung I J. Tutorial on maximum likelihood estimation. J Math Psychology, 2003, 47: 90-100 CrossRef Google Scholar

[33] Bertsekas D P. Dynamic Programming and Optimal Control. Belmont: Athena scientific, 1995. Google Scholar

[34] Wei Q, Liu D, Shi G. Multibattery Optimal Coordination Control for Home Energy Management Systems via Distributed Iterative Adaptive Dynamic Programming. IEEE Trans Ind Electron, 2015, 62: 4203-4214 CrossRef Google Scholar

[35] Bertsekas D. Distributed dynamic programming. IEEE Trans Automat Contr, 1982, 27: 610-616 CrossRef Google Scholar

[36] Tseng P. Solving H-horizon, stationary Markov decision problems in time proportional to log(H). Operations Res Lett, 1990, 9: 287-297 CrossRef Google Scholar

[37] Wen R H, Feng G, Tan W, et al. Protocol stack mapping of software defined protocol for next generation mobile networks. In: Proceedings of the IEEE International Conference on Communications (ICC), 2016. 1--6. Google Scholar


Contact and support