Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
MCOPS-SPM: Multi-Constrained Optimized Path Selection Based Spatial Pattern Matching in Social Networks Read chapter Read first chapter

2020 | OriginalPaper | Chapter

A Multi-objective Virtual Machine Scheduling Algorithm in Fault Tolerance Aware Cloud Environments

Authors : Heyang Xu, Pengyue Cheng, Yang Liu, Wei Wei, Wenjie Zhang

Published in: Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In modern cloud datacenters, virtual machine (VM) scheduling is a complex problem, especially taking consideration of the factor of service reliability. Failures may occur on physical servers while they are running cloud users’ applications. To provide high-reliability service, cloud providers can adopt some fault tolerance techniques, which will influence performance criteria of VM scheduling, such as the actual execution time and users’ expenditure. However, only few studies consider fault tolerance and its influence. In this paper, we investigate fault tolerance aware VM scheduling problem and formulate it as a bi-objective optimization model with quality of service (QoS) constraints. The proposed model tries to minimize users’ total expenditure and, at the same time maximize the successful execution rate of their VM requests. The both objectives are important concerns for users to improve their satisfactions, which can offer them sufficient incentives to stay and play in the clouds and keep the cloud ecosystem sustainable. Based on a defined cost efficiency factor, a heuristic algorithm is then developed. Experimental results show that, indeed, fault tolerance significantly influences some performance criteria of VM scheduling and the developed algorithm can decrease users’ expenditure, improve successful execution rate of their VM requests and thus perform better under fault tolerance aware cloud environments.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Review of Research on Network Flow Watermarking Techniques
next chapter PSVM: Quantitative Analysis Method of Intelligent System Risk in Independent Host Environment
Literature
1.
Armbrust, A.M., Fox, A., Griffith, R., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)
2.
Liu, L., Qiu, Z.: A survey on virtual machine scheduling in cloud computing. In: Proceedings of 2nd International Conference on Computer and Communications, Chengdu, China, pp. 2717–2721. IEEE (2016)
3.
Xu, H., Liu, Y., Wei, W., Zhang, W.: Incentive-aware virtual machine scheduling in cloud computing. J. Supercomput. 74(7), 3016–3038 (2018)
4.
Madni, S.H.H., Latiff, M.S.A., Coulibaly, Y.: Resource scheduling for infrastructure as a service (IaaS) in cloud computing: challenges and opportunities. J. Netw. Comput. Appl. 68(1), 173–200 (2016)
5.
Mann, Z.Á.: Allocation of virtual machines in cloud data centers—a survey of problem models and optimization algorithms. ACM Comput. Surv. 48(1), 11 (2015)
6.
Wang, Z., Hayat, M.M., Ghani, N., et al.: Optimizing cloud-service performance: efficient resource provisioning via optimal workload allocation. IEEE Trans. Parallel Distrib. Syst. 28(6), 1689–1702 (2017)
7.
Singh, S., Chana, I.: QRSF: QoS-aware resource scheduling framework in cloud computing. J. Supercomput. 71(1), 241–292 (2015)
8.
Zhang, R., Wu, K., Li, M., et al.: Online resource scheduling under concave pricing for cloud computing. IEEE Trans. Parallel Distrib. Syst. 27(4), 1131–1145 (2016)
9.
Xu, H., Yang, B., Qi, W., Ahene, E.: A multi-objective optimization approach to workflow scheduling in clouds considering fault recovery. KSII. Trans. Int. Inf. 10(3), 976–995 (2016)
10.
Sun, P., Wu, D., Qiu, X., Luo, L., Li, H.: Performance analysis of cloud service considering reliability. In: Proceedings of IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), Vienna, Austria, pp. 339–343. IEEE (2016)
11.
Meng, L., Sun, Y.: Context sensitive efficient automatic resource scheduling for cloud applications. In: Proceedings of the 11th International Conference on Cloud Computing, Seattle, USA, pp. 391–397 (2018)
12.
13.
Wei, L., Foh, C.H., He, B., et al.: Towards efficient resource allocation for heterogeneous workloads in IaaS clouds. IEEE Trans. Cloud Comput. 6(1), 264–275 (2018)
14.
Imai, S., Patterson, S., Varela, C. A.: Elastic virtual machine scheduling for continuous air traffic optimization. In: Proceedings of 16th International Symposium on Cluster, Cloud and Grid Computing, Cartagena, Colombia, pp. 183–186. IEEE (2016)
15.
16.
Xu, H., Yang, B.: Energy-aware resource management in cloud computing considering load balance. J. Inf. Sci. Eng. 33(1), 1–16 (2017)MathSciNet
17.
Mishra, S.K., Puthal, D., Sahoo, B., et al.: An adaptive task allocation technique for green cloud computing. J. Supercomputing 74(1), 370–385 (2018)
18.
Xu, H., Liu, Y., Wei, W., Xue, Y.: Migration cost and energy-aware virtual machine consolidation under cloud environments considering remaining runtime. Int. J. Parallel Prog. 47(3), 481–501 (2019)
19.
Wang, D., Dai, W., Zhang, C., Shi, X., Jin, H.: TPS: an efficient VM scheduling algorithm for HPC applications in cloud. In: Au, M.H.A., Castiglione, A., Choo, K.-K.R., Palmieri, F., Li, K.-C. (eds.) GPC 2017. LNCS, vol. 10232, pp. 152–164. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-57186-7_​13
20.
Kohne, A., Pasternak, D., Nagel, L., et al.: Evaluation of SLA-based decision strategies for VM scheduling in cloud data centers. In: Proceedings of the 3rd Workshop on Cross Cloud Infrastructures and Platforms, no. 6. ACM, London (2016)
21.
Zeng, L., Wang, Y., Fan, X., et al.: Raccoon: a novel network I/O allocation framework for workload-aware VM scheduling in virtual environments. IEEE Trans. Parallel Distrib. Syst. 28(9), 2651–2662 (2017)
22.
Guo, M., Guan, Q., Ke, W.: Optimal scheduling of VMs in queuing cloud computing systems with a heterogeneous workload. IEEE Access 6, 15178–15191 (2018)
23.
24.
Li, S., Zhou, Y., Jiao, L., et al.: Towards operational cost minimization in hybrid clouds for dynamic resource provisioning with delay-aware optimization. IEEE Trans. Serv. Comput. 8(3), 398–409 (2015)
25.
Somasundaram, T.S., Govindarajan, K.: CLOUDRB: a framework for scheduling and managing high-performance computing (HPC) applications in science cloud. Future Gener. Comput. Syst. 34, 47–65 (2014)
26.
Ran, Y., Yang, J., et al.: Dynamic IaaS computing resource provisioning strategy with QoS constraint. IEEE Trans. Serv. Comput. 10(2), 190–202 (2017)
27.
Sotiriadis, S., Bessis, N., Buyya, R.: Self managed virtual machine scheduling in cloud systems. Inf. Sci. 433, 381–400 (2018)
28.
Sun, P., Dai, Y., Qiu, X.: Optimal scheduling and management on correlating reliability, performance, and energy consumption for multi-agent cloud systems. IEEE Trans. Reliab. 66(2), 547–558 (2017)
29.
Xu, H., Cheng, P., Liu, L., Wei, W.: Fault tolerance aware virtual machine scheduling in cloud computing. In: Proceedings of 5th International Symposium on System and Software Reliability, Chengdu, China. IEEE (2019)
30.
Kurdi, H., Al-Anazi, A., et al.: A combinatorial optimization algorithm for multiple cloud service composition. Comput. Electr. Eng. 42, 107–113 (2015)
Metadata
Title
A Multi-objective Virtual Machine Scheduling Algorithm in Fault Tolerance Aware Cloud Environments
Authors
Heyang Xu
Pengyue Cheng
Yang Liu
Wei Wei
Wenjie Zhang
Copyright Year
2020
Book
Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications

Print ISBN: 978-3-030-48512-2

Electronic ISBN: 978-3-030-48513-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-48513-9_42

Premium Partner

    Image Credits