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Toward holistic performance management in clouds: taxonomy, challenges and opportunities

  • Nafiseh Fareghzadeh
  • Mir Ali Seyyedi
  • Mehran Mohsenzadeh
Article
  • 23 Downloads

Abstract

Cloud computing is an evolving paradigm with tremendous momentum. Performance is a major challenge in providing cloud services, and performance management is prerequisite to meet quality objectives in clouds. Although many researches have studied this issue, there is a lack of analysis on management dimensions, challenges and opportunities. As an attempt toward compensating the shortage, this work first gives a review on performance management dimensions in clouds. Moreover, a taxonomic scheme has devised to classify the recent literature, help to standardize the problem and highlight commonalities and deviations. Afterward, an autonomic and integrated performance management framework has been proposed. The proposed framework enables cloud providers to realize optimization schemes without major changes. Practicality and effectiveness of the proposed framework has been demonstrated by prototype implementation on top of the CloudSim. Experiments present promising results, in terms of the performance improvement and management. Finally, open issues, opportunities and suggestions have been presented.

Keywords

Performance management framework Quality of service Taxonomic scheme Service-level agreement Cloud computing environments 

References

  1. 1.
    Singh S, Chana I (2016) Resource provisioning and scheduling in clouds: QoS perspective. J Supercomput 72(3):926–960.  https://doi.org/10.1007/s11227-016-1626-x CrossRefGoogle Scholar
  2. 2.
    Serrano D, Bouchenak S, Kouki Y, de Oliveira Jr FA, Ledoux T, Lejeune J, Sopena J, Arantes L, Sens P (2015) SLA guarantees for cloud services. J Future Gener Comput Syst 54:233–246.  https://doi.org/10.1016/j.future.2015.03.018 CrossRefGoogle Scholar
  3. 3.
    Mehrotra R, Srivastava S, Banicescu I, Abdelwahed S (2016) Towards an autonomic performance management approach for a cloud broker environment using a decomposition-coordination based methodology. J Future Gener Comput Syst 54:195–205.  https://doi.org/10.1016/j.future.2015.03.020 CrossRefGoogle Scholar
  4. 4.
    Xu F, Liu F, Jin H, Vasilakos AV (2014) Managing performance overhead of virtual machines in cloud computing: a survey, state of the art, and future directions. Proc IEEE 102(1):11–31.  https://doi.org/10.1109/JPROC.2013.2287711 CrossRefGoogle Scholar
  5. 5.
    Wuhib F, Yanggratoke R, Stadler R (2015) Allocating compute and network resources under management objectives in large-scale clouds. J Netw Syst Manag 23(1):111–136.  https://doi.org/10.1007/s10922-013-9280-6 CrossRefGoogle Scholar
  6. 6.
    Oral A, Tekinerdogan B (2015) Supporting performance isolation in software as a service systems with rich clients. In: IEEE International Congress on Big Data, pp 297–304.  https://doi.org/10.1109/BigDataCongress.2015.49
  7. 7.
    Walraven S, De Borger W, Vanbrabant B, Lagaisse B, Van Landuyt D, Joosen W (2015) Adaptive performance isolation middleware for multi-tenant saas. In: IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pp 112-121.  https://doi.org/10.1109/UCC.2015.27
  8. 8.
    Kumara I, Han J, Colman A, Kapuruge M (2017) Software-defined service networking: performance differentiation in shared multi-tenant cloud applications. IEEE Trans Serv Comput 10(1):9–22.  https://doi.org/10.1109/TSC.2016.2594061 CrossRefGoogle Scholar
  9. 9.
    Wang W, Huang X, Qin X, Zhang W, Wei J, Zhong H (2012) Application-level cpu consumption estimation: towards performance isolation of multi-tenancy web applications. In: 5th IEEE Conference on Cloud Computing (Cloud), pp 439–446.  https://doi.org/10.1109/CLOUD
  10. 10.
    Krebs R, Spinner S, Ahmed N, Kounev S (2014) Resource usage control in multi-tenant applications. In: 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 122–131.  https://doi.org/10.1109/CCGrid.2014.80
  11. 11.
    Fareghzadeh N, Seyyedi MA, Mohsenzadeh M (2018) Dynamic performance isolation management for cloud computing services. J Supercomput 74(1):417–455.  https://doi.org/10.1007/s11227-017-2135-2 CrossRefGoogle Scholar
  12. 12.
    Patros P, MacKay SA, Kent KB, Dawson M (2016) Investigating resource interference and scaling on multi-tenant PaaS clouds. In: Proceedings of the 26th Annual International Conference on Computer Science and Software Engineering, pp 166–177Google Scholar
  13. 13.
    Krebs R, Loesch M, Kounev S (2014) Platform-as-a-service architecture for performance isolated multi-tenant applications. In: 7th IEEE International Conference on Cloud Computing (CLOUD), pp 914–921.  https://doi.org/10.1109/CLOUD.2014.125
  14. 14.
    He S, Guo L, Guo Y (2014) Elastic application container system: elastic web applications provisioning. In: Handbook of research on demand-driven web services: theory, technologies, and applications: theory, technologies, and applications, pp 376–398Google Scholar
  15. 15.
    Han R, Ghanem MM, Guo L, Guo Y, Osmond M (2014) Enabling cost-aware and adaptive elasticity of multi-tier cloud applications. J Future Gener Comput Syst 32:82–98.  https://doi.org/10.1016/j.future.2012.05.018 CrossRefGoogle Scholar
  16. 16.
    Jalili Marandi P, Gkantsidis C, Junqueira F, Narayanan D (2016) Filo: consolidated consensus as a cloud service. In: Proceeding USENIX ATC ’16 Proceedings of the 2016 USENIX Conference on Usenix Annual Technical Conference, pp 237–249Google Scholar
  17. 17.
    Ma H, Wang L, Tak BC, Wang L, Tang C (2016) Auto-tuning performance of MPI parallel programs using resource management in container-based virtual cloud. In: IEEE 9th International Conference on Cloud Computing.  https://doi.org/10.1109/CLOUD.2016.0078
  18. 18.
    Ali-Eldin A, Tordsson J, Elmroth E (2012) An adaptive hybrid elasticity controller for cloud infrastructures. In: Proceedings of 13th IEEE Network Operations and Management Symposium (NOMS 2012), pp 204–212.  https://doi.org/10.1109/NOMS.2012.6211900
  19. 19.
    Liu Y, Gureya D, Al-Shishtawy A, Vlassov V (2017) OnlineElastMan: self-trained proactive elasticity manager for cloud-based storage services. J Cluster Comput 20:1977–1994.  https://doi.org/10.1007/s10586-017-0899-z CrossRefGoogle Scholar
  20. 20.
    Teabe B, Tchana A, Hagimont D (2016) Billing system CPU time on individual VM. In: 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid).  https://doi.org/10.1109/CCGrid.2016.76
  21. 21.
    Yun H, Yao G, Pellizzoni R, Caccamo M, Sha L (2016) Memory bandwidth management for efficient performance isolation in multi-core platforms. IEEE Trans Comput 65(2):562–576.  https://doi.org/10.1109/TC.2015.2425889 MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Park J, Wang Q, Li J, Lai CA, Zhu T, Pu C (2016) Performance interference of memory thrashing in virtualized cloud environments: a study of consolidated n-tier application. In: Ninth IEEE International Conference on Cloud Computing.  https://doi.org/10.1109/CLOUD.2016.0045
  23. 23.
    Jain N, Lakshmi J (2015) PriDyn: enabling differentiated I/O services in cloud using dynamic priorities. IEEE Trans Serv Comput 8(2):212–224.  https://doi.org/10.1109/TSC.2014.2381251 CrossRefGoogle Scholar
  24. 24.
    Thereska E, Ballani H, OShea G, Karagiannis T, Rowstron A, Talpey T, Black R, Zhu T (2013) IOFlow: a software-defined storage architecture. In: Proceedings of the ACM Symposium on Operating Systems Principles (SOSP), pp 182–196.  https://doi.org/10.1145/2517349.2522723
  25. 25.
    Wu S, Tao S, Ling X, Fan H, Jin H, Ibrahim S (2015) IShare: balancing I/O performance isolation and disk I/O efficiency in virtualized environments. J Concurr Comput Pract Exp 28(2):386–399.  https://doi.org/10.1002/cpe.3496 CrossRefGoogle Scholar
  26. 26.
    Guo J, Liu F, Lui J, Jin HJ (2016) Fair network bandwidth allocation in IaaS datacenters via a cooperative game approach. IEEE Trans Netw 24(2):873–886.  https://doi.org/10.1109/TNET.2015.2389270 CrossRefGoogle Scholar
  27. 27.
    Zahid F, Gran EG, Bogdaski B, Johnsen BD, Skeie T (2016) Efficient network isolation and load balancing in multi-tenant HPC clusters. J Future Gener Comput Syst 72:145–162.  https://doi.org/10.1016/j.future.2016.04.003 CrossRefGoogle Scholar
  28. 28.
    Lee W, Kim H, Lee JY, Kim H (2017) Improving quality of multi-media services through network performance isolation in a mobile device. J Multimed Tools Appl 76(4):5317–5346.  https://doi.org/10.1007/s11042-016-3821-4 MathSciNetCrossRefGoogle Scholar
  29. 29.
    Tan H, Li C, He Z, Li K, Hwang K (2016) VMCD: a virtual multi-channel disk I/O scheduling method for virtual machines. IEEE Trans Serv Comput 9(6):982–995.  https://doi.org/10.1109/TSC.2015.2436388 CrossRefGoogle Scholar
  30. 30.
    Huang J et al (2017) Flashblox: achieving both performance isolation and uniform lifetime for virtualized ssds. In: 15th USENIX Conference on File and Storage Technologies (FAST), USENIXGoogle Scholar
  31. 31.
    Huber N, Brosig F, Spinner S, Kounev S, Bhr M (2017) Model-based self-aware performance and resource management using the descartes modeling language. IEEE Trans Softw Eng 43(5):432–452.  https://doi.org/10.1109/TSE.2016.2613863 CrossRefGoogle Scholar
  32. 32.
    Stavrinides GL, Karatza HD (2017) The impact of data locality on the performance of a SaaS cloud with real-time data-intensive. In: 21st IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT).  https://doi.org/10.1109/DISTRA.2017.8167683
  33. 33.
    Tchana A, Palma ND, Safieddine I, Hagimont D, Diot B, Vuillerme N (2015) Software consolidation as an efficient energy and cost saving solution for a SaaS/PaaS cloud model. In: European Conference on Parallel Processing (Euro-Par 2015), pp 305–316.  https://doi.org/10.1007/978-3-662-48096-0_24 Google Scholar
  34. 34.
    Addis B, Ardagna D, Panicucci B, Squillante MS, Zhang L (2013) A hierarchical approach for the resource management of very large cloud platforms. IEEE Trans Dependable Secure Comput 10(5):253–272.  https://doi.org/10.1109/TDSC.2013.4 CrossRefGoogle Scholar
  35. 35.
    Paraiso F, Merle P, Seinturier L (2016) SoCloud: a service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds. J Comput 98(5):539–565.  https://doi.org/10.1007/s00607-014-0421-x MathSciNetCrossRefGoogle Scholar
  36. 36.
    Righi R, Rodrigues VS, Andre da Costa C, Galante G, Bona L, Ferreto T (2016) Autoelastic: automatic resource elasticity for high performance applications in the cloud. IEEE Trans Cloud Comput 4(1):6–19.  https://doi.org/10.1109/TCC.2015.2424876 CrossRefGoogle Scholar
  37. 37.
    Shojafar M, Cordeschi N, Baccarelli E (2016) Energy-efficient adaptive resource management for real-time vehicular cloud services. IEEE Trans Cloud Comput 99:1–14.  https://doi.org/10.1109/TCC.2016.2551747 CrossRefGoogle Scholar
  38. 38.
    Sukhpal SG, Chana I, Singh M, Buyya R (2017) CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing. J Cluster Comput.  https://doi.org/10.1007/s10586-017-1040-z CrossRefGoogle Scholar
  39. 39.
    Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. J Concurr Comput Pract Exp 24(13):1397–1420.  https://doi.org/10.1002/cpe.1867 CrossRefGoogle Scholar
  40. 40.
    Nus A, Raz D (2014) Migration plans with minimum overall migration time. In: 2014 IEEE Network Operations and Management Symposium (NOMS).  https://doi.org/10.1109/NOMS.2014.6838358
  41. 41.
    Fu X, Zhou C (2015) Virtual machine selection and placement for dynamic consolidation in cloud computing environment. J Front Comput Sci 9(2):322–330.  https://doi.org/10.1007/s11704-015-4286-8 MathSciNetCrossRefGoogle Scholar
  42. 42.
    Xia Q, Lan Y, Xiao L (2015) A heuristic adaptive threshold algorithm on IaaS clouds. In: Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing (UIC-ATC-ScalCom), pp 399–406.  https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.89
  43. 43.
    Nishtala R, Carpenter P, Petrucci V, Martorell X (2017) Hipster: hybrid task manager for latency-critical cloud workloads. In: IEEE International Symposium on High Performance Computer Architecture (HPCA), pp 1–11.  https://doi.org/10.1109/HPCA.2017.13
  44. 44.
    Hammadi A (2017) Mathematical optimization modelling for fast-switched and delay minimized scheduling for intra cell communication in an AWGR-Based PON Data Center. J Commun Netw Syst Sci 10:13–29.  https://doi.org/10.4236/ijcns.2017.102002 CrossRefGoogle Scholar
  45. 45.
    Horri A, Mozafari MS, Dastghaibyfard G (2014) Novel resource allocation algorithms to performance and energy efficiency in cloud computing. J Supercomput 69(3):1445–1461.  https://doi.org/10.1007/s11227-014-1224-8 CrossRefGoogle Scholar
  46. 46.
    Dam S, Mandal G, Dasgupta K, Dutta P (2015) Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing. In: Third International Conference on Computer, Communication, Control and Information Technology (C3IT).  https://doi.org/10.1109/C3IT.2015.7060176
  47. 47.
    Wang L, Jing X, Duran-Limon HA, Zhao M (2015) QoS-driven cloud resource management through fuzzy model predictive control. In: IEEE International Conference on Autonomic Computing (ICAC), pp 81–90.  https://doi.org/10.1109/ICAC.2015.41
  48. 48.
    Bouabdallah R, Lajmi S, Ghedira K (2016) Use of reactive and proactive elasticity to adjust resources provisioning in the cloud provider. In: IEEE 18th International Conference on High Performance Computing and Communications (HPCC/SmartCity/DSS), pp 1155–1162.  https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0162
  49. 49.
    Gupta P, Vishwakarma L, Patel A (2014) Power-aware virtual machine consolidation considering multiple resources with live migration. J Comput Appl 103(17):24–30Google Scholar
  50. 50.
    Kumbhare AG, Simmhan Y, Frincu M, Prasanna VK (2015) Reactive resource provisioning heuristics for dynamic dataflows on cloud infrastructure. IEEE Trans Cloud Comput 3(2):105–118.  https://doi.org/10.1109/TCC.2015.2394316 CrossRefGoogle Scholar
  51. 51.
    Kumar MS, Gupta I, Panda SK, Jana PK (2017) Granularity-based workflow scheduling algorithm for cloud computing. J Supercomput 73(12):5440–5464.  https://doi.org/10.1007/s11227-017-2094-7 CrossRefGoogle Scholar
  52. 52.
    Panda SK, Jana PK (2017) An efficient request-based virtual machine placement algorithm for cloud computing. In: 13th International Conference on Distributed Computing and Internet Technology, pp 129–143.  https://doi.org/10.1007/978-3-319-50472-8_11 Google Scholar
  53. 53.
    Singh S, Chana I, Buyya R (2017) STAR: SLA-aware autonomic management of cloud resources. In: IEEE Transactions on Cloud Computing, pp 1–14.  https://doi.org/10.1109/TCC.2017.2648788
  54. 54.
    Wu L, Garg SK, Buyya R (2015) Service level agreement (SLA) based SaaS cloud management system. In: 21st IEEE International Conference on Parallel and Distributed Systems.  https://doi.org/10.1109/ICPADS.2015.62
  55. 55.
    Garcia AG, Espert IB, Garcia VH (2014) SLA-driven dynamic cloud resource management. J Future Gener Comput Syst 31:1–11.  https://doi.org/10.1016/j.future.2013.10.005 CrossRefGoogle Scholar
  56. 56.
    Son S, Kang DJ, Huh SP, Kim WY, Choi W (2016) Adaptive trade-off strategy for bargaining-based multi-objective SLA establishment under varying cloud workload. J Supercomput 72(4):1597–1622.  https://doi.org/10.1007/s11227-016-1686-y CrossRefGoogle Scholar
  57. 57.
    Khaneghah EM, Shadnoush N, Ghobakhlou AH (2018) A mathematical model to calculate real cost/performance in software distributed shared memory on computing environments. J Supercomput 74(4):1715–1764.  https://doi.org/10.1007/s11227-017-2191-7 CrossRefGoogle Scholar
  58. 58.
    Liu F, Zhou Z, Jin H, Li B, Jiang H (2014) On arbitrating the power-performance tradeoff in SaaS clouds. IEEE Trans Parallel Distrib Syst 25(10):2648–2658.  https://doi.org/10.1109/TPDS.2013.208 CrossRefGoogle Scholar
  59. 59.
    Stavrinides GL, Karatza HD (2015) A cost-effective and QoS-aware approach to scheduling real-time workflow applications in PaaS and SaaS clouds. In: Proceedings of the 3rd International Conference on Future Internet of Things and Cloud (FiCloud15), pp 231–239.  https://doi.org/10.1109/FiCloud.2015.93
  60. 60.
    Omezzine A, Saoud NBB, Tazi S, Cooperman G (2016) SLA and profit-aware SaaS provisioning through proactive renegotiation. In: 15th IEEE International Symposium on Network Computing and Applications (NCA).  https://doi.org/10.1109/NCA.2016.7778640
  61. 61.
    Huang KC, Hung CH, Hsieh W (2018) Revenue maximisation for scheduling deadline-constrained mouldable jobs on high performance computing as a service platforms. J High Perform Comput Netw (IJHPCN) 11(1):1–13.  https://doi.org/10.1504/IJHPCN.2018.088874 CrossRefGoogle Scholar
  62. 62.
    Sandholm T, Ward J, Balestrieri F, Huberman BA (2015) QoS-based pricing and scheduling of batch jobs in OpenStack clouds. In: ArXiv preprint arXiv:1504.07283
  63. 63.
    Arianyan E, Taheri H, Sharifian S (2015) Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers. J Comput Electr Eng 47:222–240.  https://doi.org/10.1016/j.compeleceng.2015.05.006 CrossRefGoogle Scholar
  64. 64.
    Sampaio AM, Barbosa JG, Prodan R (2015) PIASA: a power and interference aware resource management strategy for heterogeneous workloads in cloud data centers. J Simul Model Pract Theory 57:142–160.  https://doi.org/10.1016/j.simpat.2015.07.002 CrossRefGoogle Scholar
  65. 65.
    Ghobaei-Arani M, Shamsi M, Rahmanian AA (2017) An efficient approach for improving virtual machine placement in cloud computing environment. J Exp Theor Artif Intell 29(6):1149–1171.  https://doi.org/10.1080/0952813X.2017.1310308 CrossRefGoogle Scholar
  66. 66.
    Chiang YJ, Ouyang YC, Hsu CH (2015) An efficient green control algorithm in cloud computing for cost optimization. IEEE Trans Cloud Comput 3(2):145–155.  https://doi.org/10.1109/TCC.2014.2350492 CrossRefGoogle Scholar
  67. 67.
    Naha RK, Othman M (2016) Cost-aware service brokering and performance sentient load balancing algorithms in the cloud. J Netw Comput Appl 75:47–57.  https://doi.org/10.1016/j.jnca.2016.08.018 CrossRefGoogle Scholar
  68. 68.
    Lakra AV, Yadav DK (2015) Multi-objective tasks scheduling algorithm for cloud computing throughput optimization. J Proc Comput Sci.  https://doi.org/10.1016/j.procs.2015.04.158 CrossRefGoogle Scholar
  69. 69.
    Feng G, Buyya R (2016) Maximum revenue-oriented resource allocation in cloud. J Grid Util Comput 7(1):12–21.  https://doi.org/10.1504/IJGUC.2016.073772 CrossRefGoogle Scholar
  70. 70.
    Lu P, Sun Q, Wu K, Zhu Z (2015) Distributed online hybrid cloud management for profit-driven multi-media cloud computing. IEEE Trans Multimed 17(8):1297–1308.  https://doi.org/10.1109/TMM.2015.2441004 CrossRefGoogle Scholar
  71. 71.
    Jiankang D, Hongbo W, Shiduan C (2015) Energy-performance tradeoffs in IaaS cloud with virtual machine scheduling. IEEE China Commun 12(2):155–166.  https://doi.org/10.1109/CC.2015.7084410 CrossRefGoogle Scholar
  72. 72.
    Thiruvenkadam T, Kamalakkannan P (2015) Energy efficient multi-dimensional host load aware algorithm for virtual machine placement and optimization in cloud environment. Indian J Sci Technol 8(17):1–11.  https://doi.org/10.17485/ijst/2015/v8i17/59140 CrossRefGoogle Scholar
  73. 73.
    Hasan MS, Alvares F, Ledoux T, Pazat JL (2017) Investigating energy consumption and performance trade-off for interactive cloud application. IEEE Trans Sustain Comput 2(2):113–126.  https://doi.org/10.1109/TSUSC.2017.2714959 CrossRefGoogle Scholar
  74. 74.
    Panda SK, Jana PK (2017) SLA-based task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput 73(6):2730–2762.  https://doi.org/10.1007/s11227-016-1952-z CrossRefGoogle Scholar
  75. 75.
    Verma A, Kaushal S (2017) A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. J Parallel Comput 62:1–19.  https://doi.org/10.1016/j.parco.2017.01.002 MathSciNetCrossRefGoogle Scholar
  76. 76.
    Ibidunmoye O, Hernndez-Rodriguez F, Elmroth E (2015) Performance anomaly detection and bottleneck identification. J ACM Comput Surv 48(1):1–35.  https://doi.org/10.1145/2791120 CrossRefGoogle Scholar
  77. 77.
    Khan MA, Paplinski A, Khan AM, Murshed M, Buyya R (2018) Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management: a review. In: Rivera W (ed) Sustainable cloud and energy services.  https://doi.org/10.1007/978-3-319-62238-5_6 Google Scholar
  78. 78.
    Marcus R, Semenova S, Papaemmanouil O (2017) A learning-based service for cost and performance management of cloud databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE).  https://doi.org/10.1109/ICDE.2017.177
  79. 79.
    Hajjat M, Pn S, Sivakumar A, Rao S (2015) Measuring and characterizing the performance of interactive multi-tier cloud applications. In: IEEE International Workshop on Local and Metropolitan Area Networks (LANMAN), pp 1–6.  https://doi.org/10.1109/LANMAN.2015.7114725
  80. 80.
    Gai K, Qiu M, Zhao H (2016) Cost-aware multimedia data allocation for heterogeneous memory using genetic algorithm in cloud computing. IEEE Trans Cloud Comput 99:1–11.  https://doi.org/10.1109/TCC.2016.2594172 CrossRefGoogle Scholar
  81. 81.
    Li H, Gao X, Di Y (2015) SLA-aware resource reservation management in cloud workflows. In: Proceedings of the 27th Chinese Control and Decision Conference, Qingdao, pp 4226–4231.  https://doi.org/10.1109/CCDC.2015.7162673
  82. 82.
    Bruneo D (2014) A stochastic model to investigate data center performance and QoS in IaaS cloud computing systems. IEEE Trans Parallel Distrib Syst 25(3):560–569.  https://doi.org/10.1109/TPDS.2013.67 CrossRefGoogle Scholar
  83. 83.
    Mian R, Martin P, Vazquez-Poletti JL (2012) Provisioning data analytic workloads in a cloud. J Future Gener Comput Syst 29(6):1452–1458.  https://doi.org/10.1016/j.future.2012.01.008 CrossRefGoogle Scholar
  84. 84.
    Zhang F, Cao J, Tan W, Khan SU, Li K, Zomaya AY (2014) Evolutionary scheduling of dynamic multi-tasking workloads for big data analytics in elastic cloud. IEEE Trans Emerg Top Comput 2(3):338–351.  https://doi.org/10.1109/TETC.2014.2348196 CrossRefGoogle Scholar
  85. 85.
    Ruiz C, Jeanvoine E, Nussbaum L (2015) Performance evaluation of containers for HPC. In: Euro-Par 2015: Parallel Processing Workshops. Springer, Cham, pp 813–824.  https://doi.org/10.1007/978-3-319-27308-2_65 CrossRefGoogle Scholar
  86. 86.
    Guo L, Yan T, Zhao S, Jiang C (2014) Dynamic performance optimization for cloud computing using M/M/m queueing system. J Appl Math.  https://doi.org/10.1155/2014/756592 Google Scholar
  87. 87.
    Khatua S, Sur PK, Das RK, Mukherjee N (2014) Heuristic-based resource reservation strategies for public cloud. IEEE Trans Cloud Comput.  https://doi.org/10.1109/TCC.2014.2369434 CrossRefGoogle Scholar
  88. 88.
    de Alfonso C, Calatrava A, Molt G (2017) Container-based virtual elastic clusters. J Syst Softw 127:1–11.  https://doi.org/10.1016/j.jss.2017.01.007 CrossRefGoogle Scholar
  89. 89.
    Garg SK, Toosi AN, Gopalaiyengar SK, Buyya R (2014) SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J Netw Comput Appl 45:108–120.  https://doi.org/10.1016/j.jnca.2014.07.030 CrossRefGoogle Scholar
  90. 90.
    Calzarossa MC, Massari L, Tessera D (2016) Workload characterization: a survey revisited. ACM Comput Surv (CSUR) 48(3):1–48.  https://doi.org/10.1145/2856127 CrossRefGoogle Scholar
  91. 91.
    Song CG, Hwang NY, Yu HC, Lim JB (2017) A dynamic resource manager with effective resource isolation based on workload types in virtualized cloud computing environments. Int J Adv Sci Eng Inf Technol 7(5):1771–1776CrossRefGoogle Scholar
  92. 92.
    Khazaei H, Mii J, Mii VB (2013) Performance evaluation of cloud data centers with batch task arrivals. In: Communication Infrastructures for Cloud Computing, pp 199–223.  https://doi.org/10.4018/978-1-4666-4522-6.ch009
  93. 93.
    Chen C, He B, Tang X (2012) Green-aware workload scheduling in geographically distributed data centers .In: IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), pp 82–89.  https://doi.org/10.1109/10.1109/CloudCom.2012.6427545
  94. 94.
    Xavier MG, De Oliveira IC, Rossi FD, Dos Passos RD, Matteussi KJ, De Rose CA (2015) A performance isolation analysis of disk-intensive workloads on container-based clouds. In: 23rd Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp 253–260.  https://doi.org/10.1109/PDP.2015.67
  95. 95.
    Korkmaz M, Karsten M, Salem K, Salihoglu S (2018) Workload-aware CPU performance scaling for transactional database systems. In: SIGMOD ’18 Proceedings of the 2018 International Conference on Management of Data, pp 291–306.  https://doi.org/10.1145/3183713.3196901
  96. 96.
    Shorgin SY, Pechinkin AV, Samouylov KE, Gaidamaka YV, Gudkova IA, Sopin ES (2015) Threshold-based queuing system for performance analysis of cloud computing system with dynamic scaling. In: 12th International Conference of Numerical Analysis and Applied Mathematics ICNAAM.  https://doi.org/10.1063/1.4912509
  97. 97.
    Lin W, Wang JZ, Liang C, Qi D (2011) A threshold-based dynamic resource allocation scheme for cloud computing. Proc Eng 23:695–703.  https://doi.org/10.1016/j.proeng.2011.11.2568 CrossRefGoogle Scholar
  98. 98.
    Luo X et al (2015) Web service QoS prediction based on adaptive dynamic programming using fuzzy neural networks for cloud services. IEEE Access 3(3):2260–2269.  https://doi.org/10.1109/ACCESS.2015.2498191 CrossRefGoogle Scholar
  99. 99.
    Filieri A, Hoffmann H, Maggio M (2014) Automated design of self-adaptive software with control-theoretical formal guarantees. In: Proceedings of the 36th International Conference on Software Engineering, pp 299–310.  https://doi.org/10.1145/2568225.2568272
  100. 100.
    Chen T, Bahsoon R, Yao X (2014) Online QoS modeling in the cloud: A hybrid and adaptive multi-learners approach. In: Proceedings of the IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp 327–336.  https://doi.org/10.1109/UCC.2014.42
  101. 101.
    Palm E, Mitra K, Saguna S, Hlund C (2016) A Bayesian system for cloud performance diagnosis and prediction. In: IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp 371–374.  https://doi.org/10.1109/CloudCom.2016.0065
  102. 102.
    Calheiros RN, Ranjan R, Beloglazov A, DeRose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. J Softw Pract Exp 41:23–50.  https://doi.org/10.1002/spe.995 CrossRefGoogle Scholar
  103. 103.
    Raian A, Fabiano D, Paolo G (2010) A goal-based framework for contextual requirements modeling and analysis. J Requir Eng 15(4):439–458.  https://doi.org/10.1007/s00766-010-0110-z CrossRefGoogle Scholar
  104. 104.
    Buschmann F, Henney K, Schmidt DC (2007) A pattern language for distributed computing. Pattern-oriented software architecture, vol 4. Wiley, ChichesterGoogle Scholar
  105. 105.
    Alhamazani K et al (2015) Cross-layer multi-cloud real-time application QoS monitoring and benchmarking as-a-service framework. IEEE Trans Cloud Comput.  https://doi.org/10.1109/TCC.2015.2441715

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer Science, South of Tehran BranchIslamic Azad UniversityTehranIran

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