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


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.

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  1. 1.

    Singh S, Chana I (2016) Resource provisioning and scheduling in clouds: QoS perspective. J Supercomput 72(3):926–960.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

  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.

  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.

    Article  Google 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.

  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.

  11. 11.

    Fareghzadeh N, Seyyedi MA, Mohsenzadeh M (2018) Dynamic performance isolation management for cloud computing services. J Supercomput 74(1):417–455.

    Article  Google 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 166–177

  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.

  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 376–398

  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.

    Article  Google 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 237–249

  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.

  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.

  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.

    Article  Google 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).

  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.

    MathSciNet  MATH  Article  Google 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.

  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.

    Article  Google 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.

  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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    MathSciNet  Article  Google 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.

    Article  Google 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), USENIX

  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.

    Article  Google 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).

  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.

  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.

    Article  Google 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.

    MathSciNet  Article  Google 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.

    Article  Google 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.

    Article  Google 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.

  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.

    Article  Google Scholar 

  40. 40.

    Nus A, Raz D (2014) Migration plans with minimum overall migration time. In: 2014 IEEE Network Operations and Management Symposium (NOMS).

  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.

    MathSciNet  Article  Google 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.

  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.

  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.

    Article  Google 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.

    Article  Google 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).

  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.

  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.

  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):24–30

    Google 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.

    Article  Google 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.

    Article  Google 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.

  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.

  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.

  55. 55.

    Garcia AG, Espert IB, Garcia VH (2014) SLA-driven dynamic cloud resource management. J Future Gener Comput Syst 31:1–11.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

  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).

  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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  68. 68.

    Lakra AV, Yadav DK (2015) Multi-objective tasks scheduling algorithm for cloud computing throughput optimization. J Proc Comput Sci.

  69. 69.

    Feng G, Buyya R (2016) Maximum revenue-oriented resource allocation in cloud. J Grid Util Comput 7(1):12–21.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  74. 74.

    Panda SK, Jana PK (2017) SLA-based task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput 73(6):2730–2762.

    Article  Google 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.

    MathSciNet  Article  Google 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.

    Article  Google 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.

  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).

  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.

  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.

    Article  Google 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.

  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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

  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.

  87. 87.

    Khatua S, Sur PK, Das RK, Mukherjee N (2014) Heuristic-based resource reservation strategies for public cloud. IEEE Trans Cloud Comput.

  88. 88.

    de Alfonso C, Calatrava A, Molt G (2017) Container-based virtual elastic clusters. J Syst Softw 127:1–11.

    Article  Google 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.

    Article  Google Scholar 

  90. 90.

    Calzarossa MC, Massari L, Tessera D (2016) Workload characterization: a survey revisited. ACM Comput Surv (CSUR) 48(3):1–48.

    Article  Google 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):1771–1776

    Article  Google 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.

  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.

  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.

  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.

  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.

  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.

    Article  Google 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.

    Article  Google 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.

  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.

  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.

  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.

    Article  Google 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.

    Article  Google Scholar 

  104. 104.

    Buschmann F, Henney K, Schmidt DC (2007) A pattern language for distributed computing. Pattern-oriented software architecture, vol 4. Wiley, Chichester

    Google 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.

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Fareghzadeh, N., Seyyedi, M.A. & Mohsenzadeh, M. Toward holistic performance management in clouds: taxonomy, challenges and opportunities. J Supercomput 75, 272–313 (2019).

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