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2020 | OriginalPaper | Buchkapitel

2. A Survey of Resource Management in Cloud and Edge Computing

verfasst von : Yuchao Zhang, Ke Xu

Erschienen in: Network Management in Cloud and Edge Computing

Verlag: Springer Singapore

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Abstract

This chapter is to summarize the processing of the business, starting from the service access to the data center, to the data transmission control, to the server back-end communication, and the data synchronization service support, tracking the complete service flow of the data flow. And carry out comprehensive and in-depth research work for each of these links.

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Vorheriges Kapitel Introduction
Nächstes Kapitel A Task Scheduling Scheme in the DC Access Network
Literatur
1.
Mauve, M., Vogel, J., Hilt, V., Effelsberg, W.: Local-lag and timewarp: providing consistency for replicated continuous applications. IEEE Trans. Multimedia 6(1), 47–57 (2004)CrossRef
2.
Shao, Z., Jin, X., Jiang, W., Chen, M., Chiang, M.: Intra-data-center traffic engineering with ensemble routing. In: INFOCOM, 2013 Proceedings IEEE, pp. 2148–2156. IEEE (2013)
3.
Webb, S.D., Soh, S., Lau, W.: Enhanced mirrored servers for network games. In: Proceedings of the 6th ACM SIGCOMM Workshop on Network and System Support for Games, pp. 117–122. ACM (2007)
4.
Vik, K.-H., Halvorsen, P., Griwodz, C.: Multicast tree diameter for dynamic distributed interactive applications. In: INFOCOM 2008. The 27th Conference on Computer Communications IEEE. IEEE (2008)
5.
Guo, J., Liu, F., Zeng, D., Lui, J.C., Jin, H.: A cooperative game based allocation for sharing data center networks. In: INFOCOM, 2013 Proceedings IEEE, pp. 2139–2147. IEEE (2013)
6.
Xu, K., Zhang, Y., Shi, X., Wang, H., Wang, Y., Shen, M.: Online combinatorial double auction for mobile cloud computing markets. In: Performance Computing and Communications Conference (IPCCC), 2014 IEEE International, pp. 1–8. IEEE (2014)
7.
Seung, Y., Lam, T., Li, L.E., Woo, T.: Cloudflex: seamless scaling of enterprise applications into the cloud. In: INFOCOM, 2011 Proceedings IEEE, pp. 211–215. IEEE (2011)
8.
Yue, K., Wang, X.-L., Zhou, A.-Y., et al.: Underlying techniques for web services: a survey. J. Softw. 15(3), 428–442 (2004)MATH
9.
Zaki, Y., Chen, J., Potsch, T., Ahmad, T., Subramanian, L.: Dissecting web latency in ghana. In: Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 241–248. ACM (2014)
10.
Pujol, E., Richter, P., Chandrasekaran, B., Smaragdakis, G., Feldmann, A., Maggs, B.M., Ng, K.-C.: Back-office web traffic on the Internet. In: Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 257–270. ACM (2014)
11.
Wang, H., Shea, R., Ma, X., Wang, F., Liu, J.: On design and performance of cloud-based distributed interactive applications. In: 2014 IEEE 22nd International Conference on Network Protocols (ICNP), pp. 37–46. IEEE (2014)
12.
Dogar, F.R., Karagiannis, T., Ballani, H., Rowstron, A.: Decentralized task-aware scheduling for data center networks. ACM SIGCOMM Comput. Commun. Rev. 44, 431–442 (2014)CrossRef
13.
Alizadeh, M., Greenberg, A., Maltz, D.A., Padhye, J., Patel, P., Prabhakar, B., Sengupta, S., Sridharan, M.: Data center TCP (DCTCP). ACM SIGCOMM Comput. Commun. Rev. 41(4), 63–74 (2011)CrossRef
14.
Vamanan, B., Hasan, J., Vijaykumar, T.: Deadline-aware datacenter TCP (D2TCP). ACM SIGCOMM Comput. Commun. Rev. 42(4), 115–126 (2012)CrossRef
15.
Wilson, C., Ballani, H., Karagiannis, T., Rowtron, A.: Better never than late: meeting deadlines in datacenter networks. ACM SIGCOMM Comput. Commun. Rev. 41(4), 50–61 (2011). ACM
16.
Hong, C.-Y., Caesar, M., Godfrey, P.: Finishing flows quickly with preemptive scheduling. ACM SIGCOMM Comput. Commun. Rev. 42(4), 127–138 (2012)CrossRef
17.
Dukkipati, N., McKeown, N.: Why flow-completion time is the right metric for congestion control. ACM SIGCOMM Comput. Commun. Rev. 36(1), 59–62 (2006)CrossRef
18.
Alizadeh, M., Yang, S., Sharif, M., Katti, S., McKeown, N., Prabhakar, B., Shenker, S.: pFabric: minimal near-optimal datacenter transport. ACM SIGCOMM Comput. Commun. Rev. 43(4), 435–446 (2013). ACM
19.
Zhang, H.: More load, more differentiation – a design principle for deadline-aware flow control in DCNS. In: INFOCOM, 2014 Proceedings IEEE. IEEE (2014)
20.
Shen, M., Gao, L., Xu, K., Zhu, L.: Achieving bandwidth guarantees in multi-tenant cloud networks using a dual-hose model. In: 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC), pp. 1–8. IEEE (2014)
21.
Xu, K., Zhang, Y., Shi, X., Wang, H., Wang, Y., Shen, M.: Online combinatorial double auction for mobile cloud computing markets. In: 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC), pp.1–8. IEEE (2014)
22.
Gavish, B., Pirkul, H.: Computer and database location in distributed computer systems. IEEE Trans. Comput. (7), 583–590 (1986)CrossRef
23.
Gavish, B., Pirkul, H.: Algorithms for the multi-resource generalized assignment problem. Manag. Sci. 37(6), 695–713 (1991)CrossRefMATH
24.
Ross, G.T., Soland, R.M.: A branch and bound algorithm for the generalized assignment problem. Math. Program. 8(1), 91–103 (1975)CrossRefMathSciNetMATH
25.
Oncan, T.: A survey of the generalized assignment problem and its applications. INFOR 45(3), 123–141 (2007)MathSciNet
26.
Privault, C., Herault, L.: Solving a real world assignment problem with a metaheuristic. J. Heuristics 4(4), 383–398 (1998)CrossRefMATH
27.
Mitrović-Minić, S., Punnen, A.P.: Local search intensified: very large-scale variable neighborhood search for the multi-resource generalized assignment problem. Discret. Optim. 6(4), 370–377 (2009)CrossRefMathSciNetMATH
28.
Yagiura, M., Iwasaki, S., Ibaraki, T., Glover, F.: A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem. Discret. Optim. 1(1), 87–98 (2004)CrossRefMATH
29.
Mazzola, J.B., Wilcox, S.P.: Heuristics for the multi-resource generalized assignment problem. Nav. Res. Logist. 48(6), 468–483 (2001)CrossRefMathSciNetMATH
30.
Shtub, A., Kogan, K.: Capacity planning by the dynamic multi-resource generalized assignment problem (DMRGAP). Eur. J. Oper. Res. 105(1), 91–99 (1998)CrossRefMATH
31.
Gavranović, H., Buljubašić, M.: An efficient local search with noising strategy for Google machine reassignment problem. Ann. Oper. Res. 242, 1–13 (2014)MathSciNetMATH
32.
Sharma, P., Chaufournier, L., Shenoy, P., Tay, Y.C.: Containers and virtual machines at scale: a comparative study. In: International Middleware Conference, p. 1 (2016)
33.
Mann, Z.D., Szabó, M.: Which is the best algorithm for virtual machine placement optimization? Concurr. Comput. Pract. Exp. 29(7), e4083 (2017)CrossRef
34.
Meng, X., Pappas, V., Zhang, L.: Improving the scalability of data center networks with traffic-aware virtual machine placement. In: INFOCOM, 2010 Proceedings IEEE, pp. 1–9 (2010)
35.
Popa, L., Kumar, G., Chowdhury, M., Krishnamurthy, A., Ratnasamy, S., Stoica, I.: Faircloud: sharing the network in cloud computing. ACM SIGCOMM Comput. Commun. Rev. 42(4), 187–198 (2012)CrossRef
36.
Lacurts, K., Deng, S., Goyal, A., Balakrishnan, H.: Choreo: network-aware task placement for cloud applications. In: Conference on Internet Measurement Conference, pp. 191–204 (2013)
37.
Li, X., Wu, J., Tang, S., Lu, S.: Let’s stay together: towards traffic aware virtual machine placement in data centers. In: INFOCOM, 2014 Proceedings IEEE, pp. 1842–1850 (2014)
38.
Ma, T., Wu, J., Hu, Y., Huang, W.: Optimal VM placement for traffic scalability using Markov chain in cloud data centre networks. Electron. Lett. 53(9), 602–604 (2017)CrossRef
39.
Zhao, Y., Huang, Y., Chen, K., Yu, M., Wang, S., Li, D.S.: Joint VM placement and topology optimization for traffic scalability in dynamic datacenter networks. Comput. Netw. 80, 109–123 (2015)CrossRef
40.
Rai, A., Bhagwan, R., Guha, S.: Generalized resource allocation for the cloud. In: ACM Symposium on Cloud Computing, pp. 1–12 (2012)
41.
Wang, L., Zhang, F., Aroca, J.A., Vasilakos, A.V., Zheng, K., Hou, C., Li, D., Liu, Z.: Greendcn: a general framework for achieving energy efficiency in data center networks. IEEE J. Sel. Areas Commun. 32(1), 4–15 (2013)CrossRef
42.
Rui, L., Zheng, Q., Li, X., Jie, W.: A novel multi-objective optimization scheme for rebalancing virtual machine placement. In: IEEE International Conference on Cloud Computing, pp. 710–717 (2017)
43.
Gu, L., Zeng, D., Guo, S., Xiang, Y., Hu, J.: A general communication cost optimization framework for big data stream processing in geo-distributed data centers. IEEE Trans. Comput. 65(1), 19–29 (2015)CrossRefMathSciNetMATH
44.
Shen, M., Xu, K., Li, F., Yang, K., Zhu, L., Guan, L.: Elastic and efficient virtual network provisioning for cloud-based multi-tier applications. In: 2015 44th International Conference on Parallel Processing (ICPP), pp. 929–938. IEEE (2015)
45.
Wang, T., Xu, H., Liu, F.: Multi-resource load balancing for virtual network functions. In: IEEE International Conference on Distributed Computing Systems (2017)
46.
Taleb, T., Bagaa, M., Ksentini, A.: User mobility-aware virtual network function placement for virtual 5G network infrastructure. In: IEEE International Conference on Communications, pp. 3879–3884 (2016)
47.
Mehraghdam, S., Keller, M., Karl, H.: Specifying and placing chains of virtual network functions. In: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet), pp. 7–13. IEEE (2014)
48.
Kawashima, K., Otoshi, T., Ohsita, Y., Murata, M.: Dynamic placement of virtual network functions based on model predictive control. In: NOMS 2016 – 2016 IEEE/IFIP Network Operations and Management Symposium, pp. 1037–1042 (2016)
49.
Marotta, A., Kassler, A.: A power efficient and robust virtual network functions placement problem. In: Teletraffic Congress, pp. 331–339 (2017)
50.
Addis, B., Belabed, D., Bouet, M., Secci, S.: Virtual network functions placement and routing optimization. In: IEEE International Conference on Cloud NETWORKING, pp. 171–177 (2015)
51.
Wang, F., Ling, R., Zhu, J., Li, D.: Bandwidth guaranteed virtual network function placement and scaling in datacenter networks. In: IEEE International Performance Computing and Communications Conference, pp. 1–8 (2015)
52.
Ghaznavi, M., Khan, A., Shahriar, N., Alsubhi, K., Ahmed, R., Boutaba, R.: Elastic virtual network function placement. In: IEEE International Conference on Cloud Networking (2015)
53.
Bhamare, D., Samaka, M., Erbad, A., Jain, R., Gupta, L., Chan, H.A.: Optimal virtual network function placement in multi-cloud service function chaining architecture. Comput. Commun. 102(C), 1–16 (2017)CrossRef
54.
Zhang, Q., Xiao, Y., Liu, F., Lui, J.C.S., Guo, J., Wang, T.: Joint optimization of chain placement and request scheduling for network function virtualization. In: IEEE International Conference on Distributed Computing Systems, pp. 731–741 (2017)
55.
Taleb, T., Bagaa, M., Ksentini, A.: User mobility-aware virtual network function placement for virtual 5G network infrastructure. In: 2015 IEEE International Conference on Communications (ICC), pp. 3879–3884. IEEE (2015)
56.
Laghrissi, A., Taleb, T., Bagaa, M., Flinck, H.: Towards edge slicing: VNF placement algorithms for a dynamic & realistic edge cloud environment. In: 2017 IEEE Global Communications Conference, pp. 1–6. IEEE (2017)
57.
Prados, M.B.J., Laghrissi, A., Taleb, A.T., Taleb, T., Bagaa, M., Flinck, H.: A queuing based dynamic auto scaling algorithm for the LTE EPC control plane. In: 2018 IEEE Global Communications Conference, pp. 1–6. IEEE (2018)
58.
Bagaa, M., Taleb, T., Ksentini, A.: Service-aware network function placement for efficient traffic handling in carrier cloud. In: 2014 IEEE Wireless Communications and Networking Conference (WCNC), pp. 2402–2407. IEEE (2014)
59.
Bagaa, M., Dutra, D.L.C., Addad, R.A., Taleb, T., Flinck, H.: Towards modeling cross-domain network slices for 5G. In: 2018 IEEE Global Communications Conference, pp. 1–6. IEEE (2018)
60.
Bagaa, M., Taleb, T., Laghrissi, A., Ksentini, A.: Efficient virtual evolved packet core deployment across multiple cloud domains. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2018)
61.
Zhang, R., Zhong, A.-M., Dong, B., Tian, F., Li, R.: Container-VM-PM architecture: a novel architecture for docker container placement. In: International Conference on Cloud Computing, pp. 128–140. Springer (2018)
62.
Piraghaj, S.F., Dastjerdi, A.V., Calheiros, R.N., Buyya, R.: A framework and algorithm for energy efficient container consolidation in cloud data centers. In: 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS), pp. 368–375. IEEE (2015)
63.
Dong, Z., Zhuang, W., Rojas-Cessa, R.: Energy-aware scheduling schemes for cloud data centers on google trace data. In: 2014 IEEE Online Conference on Green Communications (OnlineGreencomm), pp. 1–6. IEEE (2014)
64.
Shi, T., Ma, H., Chen, G.: Energy-aware container consolidation based on PSO in cloud data centers. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2018)
65.
Mao, Y., Oak, J., Pompili, A., Beer, D., Han, T., Hu, P.: Draps: dynamic and resource-aware placement scheme for docker containers in a heterogeneous cluster. In: 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC), pp. 1–8. IEEE (2017)
66.
Nardelli, M., Hochreiner, C., Schulte, S.: Elastic provisioning of virtual machines for container deployment. In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, pp. 5–10. ACM (2017)
67.
Qiu, Y.: Evaluating and improving LXC container migration between cloudlets using multipath TCP. Ph.D. dissertation, Carleton University, Ottawa (2016)CrossRef
68.
Machen, A., Wang, S., Leung, K.K., Ko, B.J., Salonidis, T.: Live service migration in mobile edge clouds. IEEE Wirel. Commun. 25(1), 140–147 (2018)CrossRef
69.
Pickartz, S., Eiling, N., Lankes, S., Razik, L., Monti, A.: Migrating Linux containers using CRIU. In: International Conference on High Performance Computing, pp. 674–684. Springer (2016)
70.
Ma, L., Yi, S., Carter, N., Li, Q.: Efficient live migration of edge services leveraging container layered storage. IEEE Trans. Mob. Comput. 18, 2020–2033 (2018)CrossRef
71.
Ma, L., Yi, S., Li, Q.: Efficient service handoff across edge servers via docker container migration. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, p. 11. ACM (2017)
72.
Nadgowda, S., Suneja, S., Bila, N., Isci, C.: Voyager: complete container state migration. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 2137–2142. IEEE (2017)
73.
Li, P., Nie, H., Xu, H., Dong, L.: A minimum-aware container live migration algorithm in the cloud environment. Int. J. Bus. Data Commun. Netw. (IJBDCN) 13(2), 15–27 (2017)
74.
Guo, Y., Yao, W.: A container scheduling strategy based on neighborhood division in micro service. In: NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1–6. IEEE (2018)
75.
Kaewkasi, C., Chuenmuneewong, K.: Improvement of container scheduling for docker using ant colony optimization. In: 2017 9th International Conference on Knowledge and Smart Technology (KST), pp. 254–259. IEEE (2017)
76.
Xu, X., Yu, H., Pei, X.: A novel resource scheduling approach in container based clouds. In: 2014 IEEE 17th International Conference on Computational Science and Engineering (CSE), pp. 257–264. IEEE (2014)
77.
Zhang, X., Liu, J., Li, B., Yum, Y.-S.: CoolStreaming/DONet: a data-driven overlay network for peer-to-peer live media streaming. In: INFOCOM, vol. 3, pp. 2102–2111. IEEE (2005)
78.
79.
80.
81.
82.
Rhea, S., Godfrey, B., Karp, B., Kubiatowicz, J., Ratnasamy, S., Shenker, S., Stoica, I., Yu, H.: Opendht: a public DHT service and its uses. In: ACM SIGCOMM, vol. 35, pp. 73–84 (2005)
83.
Eyal, I., Gencer, A.E., Sirer, E.G., Van Renesse, R.: Bitcoin-NG: a scalable blockchain protocol. In: NSDI (2016)
84.
Zhu, W., Luo, C., Wang, J., Li, S.: Multimedia cloud computing. IEEE Signal Process. Mag. 28(3), 59–69 (2011)CrossRef
85.
Sripanidkulchai, K., Maggs, B., Zhang, H.: An analysis of live streaming workloads on the Internet. In: IMC, pp. 41–54. ACM (2004)
86.
Kostić, D., Rodriguez, A., Albrecht, J., Vahdat, A.: Bullet: high bandwidth data dissemination using an overlay mesh. ACM SOSP 37(5), 282–297 (2003). ACM
87.
Rodriguez, A., Albrecht, J., Bhirud, A., Vahdat, A.: Using random subsets to build scalable network services. In: USITS, pp. 19–19 (2003)
88.
Andreev, K., Maggs, B.M., Meyerson, A., Sitaraman, R.K.: Designing overlay multicast networks for streaming. In: SPAA, pp. 149–158 (2013)
89.
Alizadeh, M., Greenberg, A., Maltz, D.A., Padhye, J., Patel, P., Prabhakar, B., Sengupta, S., Sridharan, M.: Data center TCP (DCTCP). In: ACM SIGCOMM, pp. 63–74 (2010)
90.
Hong, C.Y., Caesar, M., Godfrey, P.B.: Finishing flows quickly with preemptive scheduling. ACM SIGCOMM Comput. Commun. Rev. 42(4), 127–138 (2012)CrossRef
91.
Alizadeh, M., Edsall, T., Dharmapurikar, S., Vaidyanathan, R., Chu, K., Fingerhut, A., Lam, V.T., Matus, F., Pan, R., Yadav, N.: CONGA: distributed congestion-aware load balancing for datacenters. In: ACM SIGCOMM, pp. 503–514 (2014)
92.
Zhu, Y., Eran, H., Firestone, D., Guo, C., Lipshteyn, M., Liron, Y., Padhye, J., Raindel, S., Yahia, M.H., Zhang, M.: Congestion control for large-scale RDMA deployments. ACM SIGCOMM 45(5), 523–536 (2015)CrossRef
93.
Mittal, R., Lam, V.T., Dukkipati, N., Blem, E., Wassel, H., Ghobadi, M., Vahdat, A., Wang, Y., Wetherall, D., Zats, D.: TIMELY: RTT-based congestion control for the datacenter. In: ACM SIGCOMM, pp. 537–550 (2015)
94.
Cho, I., Jang, K.H., Han, D.: Credit-scheduled delay-bounded congestion control for datacenters. In: ACM SIGCOMM, pp. 239–252 (2017)
95.
Zhang, H., Zhang, J., Bai, W., Chen, K., Chowdhury, M.: Resilient datacenter load balancing in the wild. In: ACM SIGCOMM, pp. 253–266 (2017)
96.
Nagaraj, K., Bharadia, D., Mao, H., Chinchali, S., Alizadeh, M., Katti, S.: Numfabric: fast and flexible bandwidth allocation in datacenters. In: ACM SIGCOMM, pp. 188–201 (2016)
97.
Sivaraman, A., Cheung, A., Budiu, M., Kim, C., Alizadeh, M., Balakrishnan, H., Varghese, G., McKeown, N., Licking, S.: Packet transactions: high-level programming for line-rate switches. In: ACM SIGCOMM, pp. 15–28 (2016)
98.
Chowdhury, M., Stoica, I.: Coflow: an application layer abstraction for cluster networking. In: ACM Hotnets. Citeseer (2012)
99.
Zhang, H., Chen, L., Yi, B., Chen, K., Geng, Y., Geng, Y.: CODA: toward automatically identifying and scheduling coflows in the dark. In: ACM SIGCOMM, pp. 160–173 (2016)
100.
Chen, Y., Alspaugh, S., Katz, R.H.: Design insights for MapReduce from diverse production workloads. California University Berkeley Department of Electrical Engineering and Computer Science, Technical Report (2012)CrossRef
101.
Kavulya, S., Tan, J., Gandhi, R., Narasimhan, P.: An analysis of traces from a production MapReduce cluster. In: CCGrid, pp. 94–103. IEEE (2010)
102.
Mishra, A.K., Hellerstein, J.L., Cirne, W., Das, C.R.: Towards characterizing cloud backend workloads: insights from Google compute clusters. ACM SIGMETRICS PER 37(4), 34–41 (2010)CrossRef
103.
Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H., Kozuch, M.A.: Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: Proceedings of the Third ACM Symposium on Cloud Computing, p. 7. ACM (2012)
104.
Sharma, B., Chudnovsky, V., Hellerstein, J.L., Rifaat, R., Das, C.R.: Modeling and synthesizing task placement constraints in Google compute clusters. In: SoCC, p. 3. ACM (2011)
105.
106.
Zhang, Q., Hellerstein, J.L., Boutaba, R.: Characterizing task usage shapes in Google’s compute clusters. In: LADIS (2011)
107.
Jain, S., Kumar, A., Mandal, S., Ong, J., Poutievski, L., Singh, A., Venkata, S., Wanderer, J., Zhou, J., Zhu, M., et al.: B4: experience with a globally-deployed software defined WAN. ACM SIGCOMM 43(4), 3–14 (2013)CrossRef
108.
Hong, C.-Y., Kandula, S., Mahajan, R., Zhang, M., Gill, V., Nanduri, M., Wattenhofer, R.: Achieving high utilization with software-driven WAN. In: ACM SIGCOMM, pp. 15–26 (2013)
109.
McKeown, N.: Software-defined networking. INFOCOM Keynote Talk 17(2), 30–32 (2009)
110.
111.
McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., Shenker, S., Turner, J.: Openflow: enabling innovation in campus networks. ACM SIGCOMM 38(2), 69–74 (2008)CrossRef
112.
Roberts, S.: Control chart tests based on geometric moving averages. Technometrics 1(3), 239–250 (1959)CrossRef
113.
Lucas, J.M., Saccucci, M.S.: Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 32(1), 1–12 (1990)CrossRefMathSciNet
114.
Adams, R.P., MacKay, D.J.: Bayesian online changepoint detection. arXiv preprint arXiv:0710.3742 (2007)
115.
116.
Desobry, F., Davy, M., Doncarli, C.: An online kernel change detection algorithm. IEEE Trans. Signal Process. 53(8), 2961–2974 (2005)CrossRefMathSciNetMATH
117.
118.
Smith, A.: A Bayesian approach to inference about a change-point in a sequence of random variables. Biometrika 62(2), 407–416 (1975)CrossRefMathSciNetMATH
119.
Stephens, D.: Bayesian retrospective multiple-changepoint identification. Appl. Stat. 43, 159–178 (1994)CrossRefMATH
120.
Barry, D., Hartigan, J.A.: A Bayesian analysis for change point problems. J. Am. Stat. Assoc. 88(421), 309–319 (1993)MathSciNetMATH
121.
Green, P.J.: Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82(4), 711–732 (1995)CrossRefMathSciNetMATH
122.
Berger, D.S., Gland, P., Singla, S., Ciucu, F.: Exact analysis of TTL cache networks: the case of caching policies driven by stopping times. ACM SIGMETRICS Perform. Eval. Rev. 42(1), 595–596 (2014)CrossRef
123.
Ferragut, A., Rodríguez, I., Paganini, F.: Optimizing TTL caches under heavy-tailed demands. ACM SIGMETRICS Perform. Eval. Rev. 44(1), 101–112 (2016). ACM
124.
Basu, S., Sundarrajan, A., Ghaderi, J., Shakkottai, S., Sitaraman, R.: Adaptive TTL-based caching for content delivery. ACM SIGMETRICS Perform. Eval. Rev. 45(1), 45–46 (2017)CrossRef
125.
Narayanan, A., Verma, S., Ramadan, E., Babaie, P., Zhang, Z.-L.: Deepcache: a deep learning based framework for content caching. In: Proceedings of the 2018 Workshop on Network Meets AI & ML, pp. 48–53. ACM (2018)
126.
Mao, H., Netravali, R., Alizadeh, M.: Neural adaptive video streaming with pensieve. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication, pp. 197–210. ACM (2017)
127.
Sadeghi, A., Sheikholeslami, F., Giannakis, G.B.: Optimal and scalable caching for 5G using reinforcement learning of space-time popularities. IEEE J. Sel. Top. Signal Process. 12(1), 180–190 (2018)CrossRef
128.
Li, X., Wang, X., Wan, P.-J., Han, Z., Leung, V.C.: Hierarchical edge caching in device-to-device aided mobile networks: modeling, optimization, and design. IEEE J. Sel. Areas Commun. 36(8), 1768–1785 (2018)CrossRef
129.
Ma, G., Wang, Z., Zhang, M., Ye, J., Chen, M., Zhu, W.: Understanding performance of edge content caching for mobile video streaming. IEEE J. Sel. Areas Commun. 35(5), 1076–1089 (2017)CrossRef
130.
Drolia, U., Guo, K., Tan, J., Gandhi, R., Narasimhan, P.: Cachier: edge-caching for recognition applications. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 276–286. IEEE (2017)
131.
Gabry, F., Bioglio, V., Land, I.: On energy-efficient edge caching in heterogeneous networks. IEEE J. Sel. Areas Commun. 34(12), 3288–3298 (2016)CrossRef
132.
Lombardi, A., Hörnquist, M.: Controllability analysis of networks. Phys. Rev. E 75(5) Pt 2, 056110 (2007)
133.
Liu, Y.-Y., Slotine, J.-J., Barabási, A.-L.: Controllability of complex networks. Nature 473(7346), 167 (2011)CrossRef
134.
Yuan, Z., Zhao, C., Di, Z., Wang, W.X., Lai, Y.C.: Exact controllability of complex networks. Nat. Commun. 4(2447), 2447 (2013)CrossRef
135.
Cornelius, S.P., Kath, W.L., Motter, A.E.: Realistic control of network dynamics. Nat. Commun. 4(3), 1942 (2013)CrossRef
136.
Pasqualetti, F., Zampieri, S., Bullo, F.: Controllability metrics, limitations and algorithms for complex networks. IEEE Trans. Control Netw. Syst. 1(1), 40–52 (2014)CrossRefMathSciNetMATH
137.
Francesco, S., Mario, D.B., Franco, G., Guanrong, C.: Controllability of complex networks via pinning. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 75(2), 046103 (2007)
138.
Wang, W.X., Ni, X., Lai, Y.C., Grebogi, C.: Optimizing controllability of complex networks by minimum structural perturbations. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 85(2) Pt 2, 026115 (2012)
139.
Gerla, M., Lee, E.K., Pau, G., Lee, U.: Internet of vehicles: from intelligent grid to autonomous cars and vehicular clouds. In: Internet of Things (2016)
140.
Kaiwartya, O., Abdullah, A.H., Cao, Y., Altameem, A., Liu, X.: Internet of vehicles: motivation, layered architecture network model challenges and future aspects. IEEE Access 4, 5356–5373 (2017)CrossRef
141.
Alam, K.M., Saini, M., Saddik, A.E.: Toward social Internet of vehicles: concept, architecture, and applications. IEEE Access 3, 343–357 (2015)CrossRef
Metadaten
Titel
A Survey of Resource Management in Cloud and Edge Computing
verfasst von
Yuchao Zhang
Ke Xu
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Network Management in Cloud and Edge Computing

Print ISBN: 978-981-15-0137-1

Electronic ISBN: 978-981-15-0138-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-0138-8_2