Nothing Special   »   [go: up one dir, main page]

Skip to main content

Advertisement

Log in

Service-oriented replication strategies for improving quality-of-service in cloud computing: a survey

  • Published:
Cluster Computing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The recent years have witnessed significant interest in migrating different applications into the cloud platforms. In this context, one of the main challenges for cloud applications providers is how to ensure high availability of the delivered applications while meeting users’ QoS. In this respect, replication techniques are commonly applied to efficiently handle this issue. From the literature, according to the used granularity for replication there are two major approaches to achieve replication: either through replicating the service or the underlying data. The latter one is also known as Data-oriented Replication (DoR), while the former one is referred to as Service-oriented Replication (SoR). DoR is discussed extensively in the available literature and several surveys are already published. However, SoR is still at its infancy and there is a lack of research studies. Hence, in this paper we present a comprehensive survey of SoR strategies in cloud computing. We propose a classification of existing works based on the research methods they use. Then, we carried out an in-depth study and analysis of these works. In addition, a tabular representation of all relevant features is presented to facilitate the comparison of SoR techniques and the proposal of new enhanced strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Ardagna, D., Casale, G., Ciavotta, M., Pérez, J.F., Wang, W.: Quality-of-service in cloud computing: modeling techniques and their applications. J. Internet Serv. Appl. 5(1), 11 (2014)

    Article  Google Scholar 

  2. Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Proceedings of the Grid Computing Environments Workshop (GCE’08), Austin, Texas, pp. 1–10 (2008)

  3. Endo, P.T., Rodrigues, M., Gonçalves, G.E., Kelner, J., Sadok, D.H., Curescu, C.: High availability in clouds: systematic review and research challenges. J. Cloud Comput. 5(1), 16 (2016)

    Article  Google Scholar 

  4. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R.H., Konwinski, A., Lee, G., Patterson, D.A., Rabkin, A., Stoica, I., Zaharia, M.: Above the clouds: a berkeley view of cloud computing. Tech. rep., EECS Department, University of California, Berkeley, CA, USA. http://cacs.usc.edu/education/cs653/Armbrust-CloudComp-Berkeley09.pdf (2009)

  5. Goudarzi, H., Pedram, M.: Energy-efficient virtual machine replication and placement in a cloud computing system. In: Proceedings of the IEEE Fifth International Conference on Cloud Computing (CLOUD), Honolulu, HI, USA, pp. 750–757 (2012)

  6. Wada, H., Suzuki, J., Yamano, Y., Oba, K.: Evolutionary deployment optimization for service-oriented clouds. Softw. Pract. Exp. 41(5), 469–493 (2011)

    Article  Google Scholar 

  7. Björkqvist, M., Chen, L.Y., Binder, W.: Dynamic replication in service-oriented systems. In: Proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID’12), Ottawa, Canada, pp. 531–538 (2012)

  8. Magoulès, F., Pan, J., Teng, F.: Overview of cloud computing. In: Cloud Computing Data-Intensive Computing and Scheduling, Chap. 1. Chapman & Hall/CRC (2012)

  9. Rountree, D., Castrillo, I.: Introduction to the cloud. In: The Basics of Cloud Computing, Chap. 1, pp. 1–17. Syngress (2014)

  10. Barroso, L.A., Clidaras, J., Holzle, U.: Dealing with failures and repairs. In: The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Chap. 7, pp. 101–121. Morgan & Claypool (2013)

  11. Nabi, M., Toeroe, M., Khendek, F.: Availability in the cloud: state of the art. J. Netw. Comput. Appl. 60, 54–67 (2016)

    Article  Google Scholar 

  12. Colman-Meixner, C., Develder, C., Tornatorey, M., Mukherjee, B.: A survey on resiliency techniques in cloud computing infrastructures and applications. IEEE Commun. Surv. Tutor. 18(3), 2244–2281 (2016)

    Article  Google Scholar 

  13. Avizienis, A., Laprie, J.C., Randell, B., Landwehr, C.: Basic concepts and taxonomy of dependable and secure computing. IEEE Trans. Dependable Secur. Comput. 1(1), 11–33 (2004)

    Article  Google Scholar 

  14. Leyden, J.: Chinese Trojan blocks cloud based security defences. http://www.theregister.co.uk/2011/01/20/chinese_cloud_busting_trojan (2011)

  15. Grottke, M., Matias, R., Trivedi, K.: The fundamentals of software aging. In: Proceedings of the IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Workshops), Washington, USA, pp. 1–6 (2008)

  16. Ko, R.K.L., Lee, S.S.G., Rajan, V.: Cloud computing vulnerability incidents: a statistical overview. Tech. rep., Cloud Security Alliance Cloud Vulnerabilities Working Group. https://www.cert.uy/wps/wcm/connect/certuy/abfd80ca-3142-4d28-b99c-e8f841568dde/Cloud_Computing_Vulnerability_Incidents.pdf?MOD=AJPERES (2013)

  17. Vishwanath, K.V., Nagappan, N.: Characterizing cloud computing hardware reliability. In: Proceedings of the 1st ACM Symposium on Cloud Computing (SoCC ’10), Indianapolis, Indiana, USA, pp. 193–204 (2010)

  18. Mellor, C.: Swissdisk suffers spectacular cloud snafu. http://www.theregister.co.uk/2009/10/19/swissdisk_failure/ (2009)

  19. Metz, C.: Lightning strikes Amazon cloud (honest). http://www.theregister.co.uk/2009/06/12/lightning_strikes_amazon_cloud/ (2009)

  20. Cerin, C., Coti, C., Delort, P., Diaz, F., Gagnaire, M., Mijic, M., Gaumer, Q., Guillaume, N., Lous, J.L., Lubiarz, S., Raffaelli, J., Shiozaki, K., Schauer, H., Smets, J., Séguin, L., Ville, A.: Downtime statistics of current cloud solutions. http://iwgcr.org/wp-content/uploads/2012/06/IWGCR-Paris.Ranking-002-en.pdf (2014)

  21. Elliot, S.: DevOps and the cost of downtime: fortune 1000 best practice metrics quantified, International Data Corporation (IDC). http://info.appdynamics.com/rs/appdynamics/images/DevOps-metrics-Fortune1K.pdf (2014)

  22. Ferdaus, M.H., Murshed, M., Calheiros, R.N., Buyya, R.: Network-aware virtual machine placement and migration in cloud data centers. In: Emerging Research in Cloud Distributed Computing Systems, pp. 42–91. IGI Global (2015)

  23. Yousafzai, A., Gani, A., Noor, R.M., Sookhak, M., Talebian, H., Shiraz, M., Khan, M.K.: Cloud resource allocation schemes: review, taxonomy, and opportunities. Knowl. Inf. Syst. 50(2), 347–381 (2017)

    Article  Google Scholar 

  24. Toeroe, M., Tam, F.: Service Availability: Principles and Practice. Wiley, New York (2012)

    Book  Google Scholar 

  25. Bilal, K., Khalid, O., Malik, S.U.R., Khan, M.U.S., Khan, S.U., Zomaya, A.Y.: Fault tolerance in the cloud. In: Encyclopedia of Cloud Computing, Chap. 24, pp. 291–300. Wiley (2016)

  26. Lee, L., Jeon, J., Lee, W., Jeong, S-H., Park, S-W.: QoS for web services: requirements and possible approaches. http://www.w3c.or.kr/kr-office/TR/2003/ws-qos/ (2003)

  27. Kuo, W., Wan, R.: Recent advances in optimal reliability allocation. IEEE Trans. Syst. Man Cybern. Part A 37(2), 143–156 (2007)

    Article  Google Scholar 

  28. Herbst, N.R., Kounev, S., Reussner, R.: Elasticity in cloud computing: what it is, and what it is not. In: 10th International Conference on Autonomic Computing (ICAC), California, USA, pp. 23–27 (2013)

  29. Becker, M., Lehrig, S., Becker, S.: Systematically deriving quality metrics for cloud computing systems. In: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering (ICPE 2015), Texas, USA, pp. 169–174 (2015)

  30. Lehrig, S., Eikerling, H., Becker, S.: Scalability, elasticity, and efficiency in cloud computing: a systematic literature review of definitions and metrics. In: Proceedings of the 11th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA), Montreal, Canada, pp. 83–92 (2015)

  31. Coutinho, E.F., Rego, P.A., Gomes, D.G., de Souza, J.N.: Physics and microeconomics-based metrics for evaluating cloud computing elasticity. J. Netw. Comput. Appl. 63, 159–172 (2016)

    Article  Google Scholar 

  32. Ghomi, E.J., Rahmani, A.M., Qader, N.N.: Load-balancing algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 88, 50–71 (2017)

    Article  Google Scholar 

  33. Vanitha, M., Marikkannu, P.: Effective resource utilization in cloud environment through a dynamic well-organized load balancing algorithm for virtual machines. Comput. Electr. Eng. 57, 199–208 (2017)

    Article  Google Scholar 

  34. Björkqvist, M., Chen, L., Binder, W.: Optimizing service replication in clouds. In: Proceedings of the 2011 Winter Simulation Conference (WSC’11), Arizona, USA, pp. 3307–3317 (2011)

  35. Lu, Y., Xie, Q., Kliot, G., Geller, A., Larus, J.R., Greenberg, A.: Join-idle-queue: a novel load balancing algorithm for dynamically scalable web services. Perform. Eval. 68(11), 1056–1071 (2011)

    Article  Google Scholar 

  36. Spicuglia, S., Chen, L.Y., Binder, W.: Join the best queue: reducing performance variability in heterogeneous systems. In: Proceedings of the IEEE Sixth International Conference on Cloud Computing (CLOUD), California, USA, pp. 139–146 (2013)

  37. Milani, A.S., Navimipour, N.J.: Load balancing mechanisms and techniques in the cloud environments. J. Netw. Comput. Appl. 71, 86–98 (2016)

    Article  Google Scholar 

  38. Abouzamazem, A., Ezhilchelvan, P.: Efficient inter-cloud replication for high-availability services. In: IEEE International Conference on Cloud Engineering, San Francisco, California, USA, pp. 132–139 (2013)

  39. Bonvin, N., Papaioannou, T.G., Aberer, K.: Autonomic SLA-driven provisioning for cloud applications. In: Proceedings of the 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2011), California, USA, pp. 434–443 (2011)

  40. Chen, T., Bahsoon, R., Tawil, A.R.: Scalable service-oriented replication with flexible consistency guarantee in the cloud. Inf. Sci. 264, 349–370 (2014)

    Article  MathSciNet  Google Scholar 

  41. Tabet, K., Mokadem, R., Laouar, M.R., Eom, S.B.: Data replication in cloud systems: a survey. Int. J. Inf. Syst. Soc. Change (IJISSC) 8(3), 17–33 (2017)

    Article  Google Scholar 

  42. Malik, S., Khan, S.U., Ewen, S.J., Tziritas, N., Kolodziej, J., Zomaya, A.Y., Madani, S.A., Min-Allah, N., Wang, L., Xu, C.Z., Malluhi, Q.M., Pecero, J.E., Balaji, P., Vishnu, A., Ranjan, R., Zeadally, S., Li, H.: Performance analysis of data intensive cloud systems based on data management and replication: a survey. Distrib. Parallel Databases 34(2), 1–37 (2016)

    Article  Google Scholar 

  43. Milani, B.A., Navimipour, N.J.: A comprehensive review of the data replication techniques in the cloud environments: major trends and future directions. J. Netw. Comput. Appl. 64, 229–238 (2016)

    Article  Google Scholar 

  44. Mohamed, M.F.: Service replication taxonomy in distributed environments. Serv. Oriented Comput. Appl. 10(3), 317–336 (2016)

    Article  Google Scholar 

  45. Bonvin, N., Papaioannou, T.G., Aberer, K.: An economic approach for scalable and highly-available distributed applications. In: Proceedings of the IEEE 3rd International Conference on Cloud Computing (CLOUD), Miami, Forida, USA, pp. 498–505 (2010)

  46. Wei, Q., Veeravalli, B., Gong, B., Zeng, L., Feng, D.: CDRM: a cost-effective dynamic replication management scheme for cloud storage cluster. In: Proceedings of the IEEE International Conference on Cluster Computing, Heraklion, Greece, pp. 188–196 (2010)

  47. Nguyen, T., Cutway, A., Shi, W.: Differentiated replication strategy in data centers. In: Proceedings of the IFIP International Conference, Network and Parallel Computing (NPC 2010), Zhengzhou, China, pp. 277–288 (2010)

  48. Tran, K., Agoulmine, N.: Adaptive and cost-effective service placement. In: Proceedings of the Global Communications Conference, GLOBECOM 2011, 5-9 December 2011, Houston, Texas, USA, pp. 1–6 (2011)

  49. Tsai, W.T., Zhong, P., Elston, J., Bai, X., Chen, Y.: Service replication strategies with mapreduce in clouds. In: Proceedings of the 10th International Symposium on Autonomous Decentralized Systems (ISADS), Tokyo & Hiroshima, Japan, pp. 381–388 (2011)

  50. Li, W., Yang, Y., Yuan, D.: A novel cost-effective dynamic data replication strategy for reliability in cloud data centres. In: Proceedings of the IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC), Sydney, Australia, pp. 496–502 (2011)

  51. Björkqvist, M., Chen, L.Y., Binder, W.: Opportunistic service provisioning in the cloud. In: Proceedings of the IEEE Fifth International Conference on Cloud Computing (CLOUD), Honolulu, HI, USA, pp. 237–244 (2012)

  52. Sun, D., Chang, G., Gao, S., Jin, L., Wang, X.: Modeling a dynamic data replication strategy to increase system availability in cloud computing environments. J. Comput. Sci. Technol. 27(2), 256–272 (2012)

    Article  MATH  Google Scholar 

  53. Ooi, B.Y., Chan, H.Y., Cheah, Y.N.: Dynamic service placement and replication framework to enhance service availability using team formation algorithm. J. Syst. Softw. 85(9), 2048–2062 (2012)

    Article  Google Scholar 

  54. Qu, Y., Xiong, N.: RFH: A resilient, fault-tolerant and high-efficient replication algorithm for distributed cloud storage. In: Proceedings of the 41st International Conference on Parallel Processing, Pittsburgh, Pennsylvania, USA, pp. 520–529 (2012)

  55. Kim, K.: Performance analysis for multiple service-oriented applications sharing reusable services in the cloud. Int. J. Adv. Comput. Technol. (IJACT) 5(12), 387–395 (2013)

    Google Scholar 

  56. Ye, Z., Li, S., Zhou, X.: GC place: Geo-cloud based correlation aware data replica placement. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing (SAC’13), Coimbra, Portugal, pp. 371–376 (2013)

  57. Luo, H., Liu, X., Liu, J.: Queueing theory based service replica strategy for business process efficiency optimization in community cloud. In: Proceedings of the International Conference on Cloud Computing and Big Data (CCBD), Wuhan, China, pp. 83–90 (2014)

  58. Lin, J., Chen, C., Chang, J.M.: Qos-aware data replication for data intensive applications in cloud computing systems. IEEE Trans. Cloud Comput. 1(1), 101–115 (2013)

    Article  Google Scholar 

  59. Ghanbari, H., Litoiu, M., Pawluk, P., Barna, C.: Replica placement in cloud through simple stochastic model predictive control. In: Proceedings of the IEEE 7th International Conference on Cloud Computing (CLOUD), Alaska, USA, pp. 80–87 (2014)

  60. Bai, X., Jin, H., Liao, X., Shi, X., Shao, Z.: RTRM: a response time-based replica management strategy for cloud storage system. In: Proceedings of the International Conference on Grid and Pervasive Computing (GPC), Daegu, Korea, pp. 124–133 (2013)

  61. Yusoh, Z., Tang, M.: Composite SaaS scaling in cloud computing using a hybrid genetic algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Beijing, China, pp. 1609–1616 (2014)

  62. Long, S.Q., Zhao, Y.L., Chen, W.: MORM: a multi-objective optimized replication management strategy for cloud storage cluster. J. Syst. Architect. 60(2), 234–244 (2014)

    Article  Google Scholar 

  63. Gullhav, A.N., Nygreen, B.: Deployment of replicated multi-tier services in cloud data centres. Int. J. Cloud Comput. (IJCC) 4(2), 130–149 (2015)

    Article  Google Scholar 

  64. Gullhav, A.N., Nygreen, B.: A branch and price approach for deployment of multi-tier software services in clouds. J. Comput. Oper. Res. 75, 12–27 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  65. Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., Zomaya, A.Y.: Energy-efficient data replication in cloud computing datacenters. Clust. Comput. 18(1), 385–402 (2015)

    Article  Google Scholar 

  66. Wu, J., Zhang, B., Yang, L., Wang, P., Zhang, C.: A replicas placement approach of component services for service-based cloud application. Clust. Comput. 19(2), 709–721 (2016)

    Article  Google Scholar 

  67. Gill, N.K., Singh, S.: A dynamic, cost-aware, optimized data replication strategy for heterogeneous cloud data centers. Future Gener. Comput. Syst. 65, 10–32 (2016)

    Article  Google Scholar 

  68. Gullhav, A.N., Cordeau, J.F., Hvattum, L.M., Nygreen, B.: Adaptive large neighborhood search heuristics for multi-tier service deployment problems in clouds. Eur. J. Oper. Res. 259(3), 829–846 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  69. Tos, U., Mokadem, R., Hameurlain, A., Ayav, T., Bora, S.: A performance and profit oriented data replication strategy for cloud systems. In: Proceedings of the IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, pp. 780–787 (2016)

  70. Slimani, S., Hamrouni, T., Charrada, F.B., Magoulès, F.: Ddsor: A dependency aware dynamic service replication strategy for efficient execution of service-oriented applications in the cloud. In: Proceedings of the International Conference on High Performance Computing Simulation (HPCS), Genoa, Italy, pp. 603–610 (2017)

  71. Mansouri, N.: Adaptive data replication strategy in cloud computing for performance improvement. Front. Comput. Sci. 10(5), 925–935 (2016)

    Article  Google Scholar 

  72. Mansouri, N., Rafsanjani, M.K., Javidi, M.M.: DPRS: a dynamic popularity aware replication strategy with parallel download scheme in cloud environments. Simul. Model. Pract. Theory 77, 177–196 (2017)

    Article  Google Scholar 

  73. Arani, M.G., Jabbehdari, S., Pourmina, M.A.: An autonomic resource provisioning approach for service-based cloud applications: a hybrid approach. Future Gener. Comput. Syst. 78, 191–210 (2018)

    Article  Google Scholar 

  74. Sousa, F.R., Moreira, L.O., Costa Filho, J.S., Machado, J.C.: Predictive elastic replication for multi-tenant databases in the cloud. Concurr. Comput. Pract. Exp. 30, 1–15 (2018)

    Article  Google Scholar 

  75. Sun, S., Yao, W., Li, X.: DARS: a dynamic adaptive replica strategy under high load cloud-p2p. Future Gener. Comput. Syst. 78, 31–40 (2018)

    Article  Google Scholar 

  76. Tabet, K., Mokadem, R., Laouar, M.R.: A data replication strategy for document-oriented NoSQL systems. Int. J. Grid Util. Comput. 10(1), 53–62 (2019)

    Article  Google Scholar 

  77. Limam, S., Mokadem, R., Belalem, G.: Data replication strategy with satisfaction of availability, performance and tenant budget requirements. Clust. Comput. 22(4), 1199–1210 (2019)

    Article  Google Scholar 

  78. Ebadi, Y., Navimipour, N.J.: An energy-aware method for data replication in the cloud environments using a tabu search and particle swarm optimization algorithm. Concurr. Comput. 31(1), e4757 (2019)

    Article  Google Scholar 

  79. Little, M.C.: Object replication in a distributed system. Ph.D. thesis, Faculty of Informatics, the University of Newcastle upon Tyne Computing Laboratory (1991)

  80. Liu, L., Wu, Z., Ma, Z., Cai, Y.: A dynamic fault tolerant algorithm based on active replication. In: Proceedings of the Seventh International Conference on Grid and Cooperative Computing (GCC ’08), Shenzhen, China, pp. 557–562 (2008)

  81. Mendonça, N.C., Silva, J.A.F., Anido, R.O.: Client-side selection of replicated web services: an empirical assessment. J. Syst. Softw. 81(8), 1346–1363 (2008)

    Article  Google Scholar 

  82. Mohamed, M.F., ElYamany, H.F., Nassar, H.M.: A study of an adaptive replication framework for orchestrated composite web services. SpringerPlus 2(1), 1–18 (2013)

    Article  Google Scholar 

  83. Ramalingam, S., Mohandas, L.: A fuzzy based sensor web for adaptive prediction framework to enhance the availability of web service. Int. J. Distrib. Sens. Netw. 12(12), 4972061 (2016)

    Article  Google Scholar 

  84. Salas, J., Perez-Sorrosal, F., Patiño Martínez, M., Jiménez-Peris, R.: Ws-replication: a framework for highly available web services. In: Proceedings of the 15th International Conference on World Wide Web (WWW ’06), Edinburgh, Scotland, pp. 357–366 (2006)

  85. Sundharam, R., Lakshmi, M., Abarajithan, D.: Enhancing the availability of web services for mission critical applications. In: Proceedings of the International Conference on Trendz in Information Sciences & Computing in Information Sciences Computing (TISC2010), Sathyabama University, Jeppiaar Nagar, Rajiv Gandhi Salai, India, pp. 149–151 (2010)

  86. Ye, X., Shen, Y.: A middleware for replicated web services. In: Proceedings of the IEEE International Conference on Web Services (ICWS ’05), Washington, DC, USA, pp. 631–638 (2005)

  87. Björkqvist, M.: Resoure management of repliated service systems provisioned in the cloud. Ph.D. thesis, Università della Svizzera Italiana (2015). Faculty of Informatics. https://doc.rero.ch/record/255690/files/2015INFO005.pdf

  88. Luo, H., Liu, J., Liu, X.: A two-stage service replica strategy for business process efficiency optimization in community cloud. Chin. J. Electron. 26(1), 80–87 (2017)

    Article  Google Scholar 

  89. Chen, Y., Li, Y.: Computational Intelligence Assisted Design In Industrial Revolution 4.0. Taylor & Francis, New York (2018)

    Book  Google Scholar 

  90. Gullhav, A.N.: Optimization-based resource allocation in cloud computing. Ph.D. thesis, Norwegian University of Science and Technology (2016)

  91. Maurer, M., Breskovic, I., Emeakaroha, V., Brandic, I.: Revealing the mape loop for the autonomic management of cloud infrastructures. In: Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Corfu, Greece, pp. 147–152 (2011)

  92. Watkins, C.J.C.H.: Learning from delayed rewards. Ph.D. thesis, King’s College, Cambridge, UK. http://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf (1989)

  93. Ooi, B.Y., Chan, H.Y., Cheah, Y.N.: Dynamic service placement and redundancy to ensure service availability during resource failures. In: Proceedings of the International Symposium in Information Technology (ITSim), pp. 715–720 (2010)

  94. Tsai, W.T., Sun, X., Shao, Q., Qi, G.: Two-tier multi-tenancy scaling and load balancing. In: Proceedings of the IEEE 7th International Conference on E-Business Engineering, Shanghai, China, pp. 484–489 (2010)

  95. Gullhav, A.N., Nygreen, B., Heegaard, P.E.: Approximating the response time distribution of fault-tolerant multitier cloud services. In: Proceedings of the IEEE/ACM 6th International Conference on Utility and Cloud Computing (UCC), Dresden, Germany, pp. 287–291 (2013)

  96. Qu, C., Calheiros, R.N., Buyya, R.: Auto-scaling web applications in clouds: a taxonomy and survey. ACM Comput. Surv. (CSUR) 51(4), 1–33 (2018)

    Article  Google Scholar 

  97. Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A review of auto-scaling techniques for elastic applications in cloud environments. J. Grid Comput. 12(4), 559–592 (2014)

    Article  Google Scholar 

  98. Nikravesh, A.Y., Ajila, S.A., Lung, C.H.: An autonomic prediction suite for cloud resource provisioning. J. Cloud Comput. 6(1), 3 (2017)

    Article  Google Scholar 

  99. Atrey, A., Moens, H., Seghbroeck, G., Volckaert, B., Turck, F.: Design and evaluation of automatic workflow scaling algorithms for multi-tenant saas. In: In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016), pp. 221–229

  100. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

    Article  Google Scholar 

  101. IBM Cloud Education. Containerization. https://www.ibm.com/cloud/learn/containerization (2019)

  102. Pahl, C., Brogi, A., Soldani, J., Jamshidi, P.: Cloud container technologies: a state-of-the-art review. IEEE Trans. Cloud Comput. 7(3), 677–692 (2019)

    Article  Google Scholar 

  103. Ruan, B., Huang, H., Wu, S., Jin, H.: A performance study of containers in cloud environment. In: Advances in Services Computing—10th Asia-Pacific Services Computing Conference, APSCC 2016, Zhangjiajie, China, pp. 343–356 (2016)

  104. Networkx library. http://networkx.lanl.gov

  105. Mahdian, M., Markakis, E., Saberi, A., Vazirani, V.: A greedy facility location algorithm analyzed using dual fitting. In: Proceedings of the 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems and 5th International Workshop on Randomization and Approximation, Lecture Notes in Computer Science, vol. 2129, pp. 127–137 (2001)

  106. Jamin, J.W.S.: Inet-3.0: Internet topology generator, http://topology.eecs.umich.edu/inet/inet-3.0.pdf. Tech. rep. (2002)

  107. Scherfke, S.: Discrete-event simulation with SimPy. https://simpy.readthedocs.io/en/latest/ (2014)

  108. Clarknet-http-two weeks of http logs from the clarknet www server. http://ita.ee.lbl.gov/html/contrib/ClarkNet-HTTP.html

  109. Nasa-http-two months of http logs from the kscnasa www server. http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html

  110. Yang, J., Liu, C., Shang, Y., Cheng, B., Mao, Z., Liu, C., Niu, L., Chen, J.: A cost-aware auto-scaling approach using the workload prediction in service clouds. Inf. Syst. Front. 16(1), 7–18 (2014)

    Article  Google Scholar 

  111. N. Roy A. Dubey, A.S.G.: Efficient auto-scaling in the cloud using predictive models for workload forecasting. In: Proceedings of the fourth IEEE International Conference on Cloud Computing (CLOUD), Washington, USA, pp. 500–507 (2011)

  112. Al-Ayyoub, M., Jararweh, Y., Daraghmeh, M., Althebyan, Q.: Multi- agent based dynamic resource provisioning and monitoring for cloud computing systems infrastructure. Clust. Comput. 18(2), 919–932 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sarra Slimani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Slimani, S., Hamrouni, T. & Ben Charrada, F. Service-oriented replication strategies for improving quality-of-service in cloud computing: a survey. Cluster Comput 24, 361–392 (2021). https://doi.org/10.1007/s10586-020-03108-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-020-03108-z

Keywords

Navigation