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Abstract: The fifth-generation (5G) network cloudification enables third parties to deploy their applications (e.g., edge caching and edge computing) at the network edge. Many previous works have focused on specific service strategies (e.g., cache placement strategy and vCPU provision strategy) for edge applications from the perspective of a certain third party by maximizing its benefit. However, there is no literature that focuses on how to efficiently allocate resources from the perspective of a mobile network operator, taking the different deployment requirements of all third parties into consideration. In this paper, we address the problem by formulating an optimization problem, which minimizes the total deployment cost of all third parties. To capture the deployment requirements of the third parties, the applications that they want to deploy are classified into two types, namely, computation-intensive ones and storage-intensive ones, whose requirements are considered as input parameters or constraints in the optimization. Due to the NP-hardness and non-convexity of the formulated problem, we have designed an elitist genetic algorithm that converges to the global optimum to solve it. Extensive simulations have been conducted to illustrate the feasibility and effectiveness of the proposed algorithm.

分级移动边缘云中节省开销的资源分配

[1]Abbas N, Zhang Y, Taherkordi A, et al., 2018. Mobile edge computing: a survey. IEEE Int Things J, 5(1):450-465.

[2]Alicherry M, Lakshman TV, 2012. Network aware resource allocation in distributed clouds. Proc IEEE INFOCOM, p.963-971.

[3]Baktir AC, Ozgovde A, Ersoy C, 2017. How can edge computing benefit from software-defined networking: a survey, use cases, and future directions. IEEE Commun Surv Tutor, 19(4):2359-2391.[doi:10.1109/COMST.2017.2717482]

[4]Bilal K, Erbad A, 2017. Edge computing for interactive media and video streaming. Proc 2$^rm nd$ Int Conf on Fog and Mobile Edge Computing, p.68-73.

[5]Bouet M, Conan V, 2018. Mobile edge computing resources optimization: a geo-clustering approach. IEEE Trans Netw Serv Manag, 15(2):787-796.

[6]Burer S, Letchford AN, 2012. Non-convex mixed-integer nonlinear programming: a survey. Surv Oper Res Manag Sci, 17(2):97-106.

[7]China Telecom, 2016. China Telecom CTNet2025 Network Architecture White Paper (in Chinese).

[8]China Unicom, 2018. White Paper for China Unicom’s Edge-Cloud Service Platform Architecture and Industrial Eco-system. China Unicom Network Technology Research Institute.

[9]Chu PC, Beasley JE, 1997. A genetic algorithm for the generalised assignment problem. Comput Oper Res, 24(1): 17-23.

[10]Ding QH, Pang HT, Sun LF, 2017. SAM: cache space allocation in collaborative edge-caching network. Proc IEEE Int Conf on Communications, p.1-6.

[11]Enayet A, Razzaque MA, Hassan MM, et al., 2018. A mobility-aware optimal resource allocation architecture for big data task execution on mobile cloud in smart cities. IEEE Commun Mag, 56(2):110-117.

[12]Ghoreishi SE, Friderikos V, Karamshuk D, et al., 2016. Provisioning cost-effective mobile video caching. Proc IEEE Int Conf on Communications, p.1-7.

[13]Gudipati A, Perry D, Li LE, et al., 2013. SoftRAN: software defined radio access network. Proc 2$^rm nd$ ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking, p.25-30.

[14]Lai ZQ, Hu YC, Cui Y, et al., 2017. Furion: engineering high-quality immersive virtual reality on today’s mobile devices. Proc 23^rd Annual Int Conf on Mobile Computing and Networking, p.409-421.

[15]Mach P, Becvar Z, 2017. Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun Surv Tutor, 19(3):1628-1656.

[16]Mann ZÁ 2015. Allocation of virtual machines in cloud data centers—a survey of problem models and optimization algorithms. ACM Comput Surv, 48(1), Article 11.

[17]Manzalini A, Minerva R, Callegati F, et al., 2013. Clouds of virtual machines in edge networks. IEEE Commun Mag, 51(7):63-70.

[18]Mao YY, You CS, Zhang J, et al., 2017. A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor, 19(4):2322-2358.

[19]MEC, 2016. Multi-access Edge Computing (MEC); Framework and Reference Architecture. ETSI GS MEC 003 v2.1.1. https://www.etsi.org/deliver/etsi_gs/MEC/ 001_099/003/02.01.01_60/gs_MEC003v020101p.pdf

[20]MEC, 2018. Mobile Edge Computing (MEC); Deployment of Mobile Edge Computing in an NFV Environment. ETSI GR MEC 017 v1.1.1. https://www.etsi.org/deliver/ etsi_gr/MEC/001_099/017/01.01.01_60/gr_MEC017 v010101p.pdf

[21]Michalewicz Z, Schoenauer M, 1996. Evolutionary algorithms for constrained parameter optimization problems. Evol Comput, 4(1):1-32.

[22]Peng X, Zhang J, Song SH, et al., 2016. Cache size allocation in backhaul limited wireless networks. Proc IEEE Int Conf on Communications, p.1-6.

[23]Roman R, Lopez J, Mambo M, 2018. Mobile edge computing, Fog et al.: a survey and analysis of security threats and challenges. Fut Gener Comput Syst, 78:680-698.

[24]Sama MR, Contreras LM, Kaippallimalil J, et al., 2015. Software-defined control of the virtualized mobile packet core. IEEE Commun Mag, 53(2):107-115.

[25]Sousa B, Cordeiro L, Sim oes P, et al., 2016. Toward a fully cloudified mobile network infrastructure. IEEE Trans Netw Serv Manag, 13(3):547-563.

[26]Taleb T, Dutta S, Ksentini A, et al., 2017. Mobile edge computing potential in making cities smarter. IEEE Commun Mag, 55(3):38-43.

[27]Tan LZ, Tan YY, Yun GX, et al., 2016. Genetic algorithms based on clustering for traveling salesman problems. Proc 12^th Int Conf on Natural Computation, Fuzzy Systems and Knowledge Discovery, p.103-108.

[28]Tang JH, Quek TQS, Tay WP, 2016. Joint resource segmentation and transmission rate adaptation in Cloud RAN with Caching as a Service. Proc IEEE 17^th$ Int Workshop on Signal Processing Advances in Wireless Communications, p.1-6.

[29]Tong L, Li Y, Gao W, 2016. A hierarchical edge cloud architecture for mobile computing. Proc 35^th Annual IEEE Int Conf on Computer Communications, p.1-9.

[30]Wang S, Zhang X, Zhang Y, et al., 2017. A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access, 5:6757-6779.

[31]Wang SQ, Zafer M, Leung KK, 2017. Online placement of multi-component applications in edge computing environments. IEEE Access, 5:2514-2533.

[32]Wang W, Zhao YL, Tornatore M, et al., 2017. Virtual machine placement and workload assignment for mobile edge computing. Proc IEEE 6^th Int Conf on Cloud Networking, p.1-6.

[33]Wang XF, Chen M, Taleb T, et al., 2014. Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag, 52(2):131-139.

[34]Yin H, Zhang X, Liu HH, et al., 2017. Edge provisioning with flexible server placement. IEEE Trans Parall Distrib Syst, 28(4):1031-1045.

[35]Zhang HK, Quan W, Chao HC, et al., 2016. Smart identifier network: a collaborative architecture for the future Internet. IEEE Netw, 30(3):46-51.

[36]Zhang S, Zhang N, Yang P, et al., 2017. Cost-effective cache deployment in mobile heterogeneous networks. IEEE Trans Veh Technol, 66(12):11264-11276.

[37]Zhou H, Wang H, Li XH, et al., 2018. A survey on mobile data offloading technologies. IEEE Access, 6:5101-5111.