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The Journal of Supercomputing

Erschienen in:

10.11.2018

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

verfasst von: Nafiseh Fareghzadeh, Mir Ali Seyyedi, Mehran Mohsenzadeh

Erschienen in: The Journal of Supercomputing | Ausgabe 1/2019

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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.

Vorheriger Artikel Mesh-of-Torus: a new topology for server-centric data center networks

Nächster Artikel Mobility-aware task delegation model in mobile cloud computing

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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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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

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

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 CrossRef

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.

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.

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 CrossRef

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.

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.

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.

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 CrossRef

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.

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.

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.

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 CrossRef

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.

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 MathSciNetMATHCrossRef

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.

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 CrossRef

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.

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 CrossRef

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 CrossRef

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 CrossRef

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 MathSciNetCrossRef

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 CrossRef

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.

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 CrossRef

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.

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

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 CrossRef

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 MathSciNetCrossRef

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 CrossRef

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 CrossRef

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

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 CrossRef

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.

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 MathSciNetCrossRef

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.

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.

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 CrossRef

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 CrossRef

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.

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.

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.

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

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 CrossRef

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 CrossRef

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

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.

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.

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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.

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.

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 CrossRef

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.

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 MathSciNetCrossRef

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 CrossRef

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

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.

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.

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 CrossRef

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.

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 CrossRef

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 CrossRef

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 CrossRef

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

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

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

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 CrossRef

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 CrossRef

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 CrossRef

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):1771–1776CrossRef

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.

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.

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.

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.

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.

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 CrossRef

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 CrossRef

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.

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.

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.

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 CrossRef

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 CrossRef

104.

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

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

Titel: Toward holistic performance management in clouds: taxonomy, challenges and opportunities
verfasst von: Nafiseh Fareghzadeh
Mir Ali Seyyedi
Mehran Mohsenzadeh
Publikationsdatum: 10.11.2018
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 1/2019
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-018-2679-9

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