Skip to main content

An Empirical Study of Different Techniques for the Improvement of Quality of Service in Cloud Computing

  • Conference paper
  • First Online:
Data Engineering for Smart Systems

Abstract

In a large, heterogeneous, and distributed environment, the computing infrastructure expands, and resource management becomes a challenging task. In a cloud world, one experiences problems of resource distribution, triggered by items like heterogeneity, dynamism, and errors, with uncertainty and distribution of resource. Unfortunately, to manage these environments, applications, and resource behaviors, current resource management techniques, frameworks, and mechanisms are insufficient. In recent years, the computer system is mostly based on cloud computing. Service level agreement (SLA) and quality of service (QoS) decrease by the minimum utilization of resources. Proper utilization of resources reduces the SLA violation and maximize QoS. Proper management of resources managed by service algorithms. This research paper analyzes the different types of resource management strategies which play the vital role mange the resources computing resources.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Malekloo M-H, Kara N, El Barachi M (2018) An energy-efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustain Comput Inf Syst 17:9–24

    Google Scholar 

  2. Tarafdar A, Debnath M, Khatua S, Das RK (2020) Energy and quality of service-aware virtual machine consolidation in a cloud data center. J Supercomput 1–32

    Google Scholar 

  3. Haghshenas K, Mohammadi S (2020) Prediction-based underutilized and destination host selection approaches for energy-efficient dynamic VM consolidation in data centers. J Supercomput 1–18

    Google Scholar 

  4. Mandal R, Mondal MK, Banerjee S, Biswas U (2020) An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing. J Supercomput 1–20

    Google Scholar 

  5. Gul B, Khan IA, Mustafa S, Khalid O, Hussain SS, Dancey D, Nawaz R (2020) CPU and RAM energy-based SLA-aware workload consolidation techniques for clouds.” IEEE Access 8: 62990–63003

    Google Scholar 

  6. Yaghoubi M, Maroosi A (2020) Simulation and modeling of an improved multi-verse optimization algorithm for QoS-aware web service composition with service level agreements in the cloud environments. Simul Modell Pract Theory 102090

    Google Scholar 

  7. Rajabzadeh M, Haghighat AT, Rahmani AM (2020) New comprehensive model based on virtual clusters and absorbing Markov chains for energy-efficient virtual machine management in cloud computing. J Supercomput 1–20

    Google Scholar 

  8. Gupta A, Bhadauria HS, Singh A (2020) SLA-aware load balancing using risk management framework in cloud. J Ambient Intell Humaniz Comput 1–10

    Google Scholar 

  9. Li Z, Xinrong Yu, Lei Yu, Guo S, Chang V (2020) Energy-efficient and quality-aware VM consolidation method. Futur Gener Comput Syst 102:789–809

    Article  Google Scholar 

  10. Bharathi PD, Prakash P, Kiran MVK (2017) Energy efficient strategy for task allocation and VM placement in cloud environment. In: 2017 Innovations in power and advanced computing technologies (i-PACT). IEEE, pp 1–6

    Google Scholar 

  11. Goraya MS, Singh D (2020) Satisfaction aware QoS-based bidirectional service mapping in cloud environment. Clust Comput 1–21

    Google Scholar 

  12. Ali SA, Affan M, Alam M (2018) A study of efficient energy management techniques for cloud computing environment. arXiv:1810.07458

  13. Haghighi MA, Maeen M, Haghparast M (2019) An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms. Wirel Pers Commun 104(4):1367–1391

    Google Scholar 

  14. Raza MR, Varol A (2020) QoS parameters for viable SLA in Cloud. In: 2020 8th international symposium on digital forensics and security (ISDFS). IEEE, pp 1–5

    Google Scholar 

  15. Hsieh S-Y, Liu C-S, Buyya R, Zomaya AY (2020) Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers. J Parallel Distrib Comput 139:99–109

    Article  Google Scholar 

  16. Saadi Y, Kafhali SE (2020) Energy-efficient strategy for virtual machine consolidation in cloud environment. Soft Comput 1–15

    Google Scholar 

  17. Kumar GG, Vivekanandan P (2019) Energy efficient scheduling for cloud data centers using heuristic based migration. Clust Comput 22(6):14073–14080

    Google Scholar 

  18. Khoshkholghi MA, Derahman MN, Abdullah A, Subramaniam S, Othman M (2017) Energy-efficient algorithms for dynamic virtual machine consolidation in cloud data centers. IEEE Access 5:10709–10722

    Google Scholar 

  19. Stavrinides GL, Karatza HD (2019) An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations. Futur Gener Comput Syst 96:216–226

    Article  Google Scholar 

  20. Bhattacherjee S, Das R, Khatua S, Roy S (2019) Energy-efficient migration techniques for cloud environment: a step toward green computing. J Supercomput 1–29

    Google Scholar 

  21. Jain M, Priya A (2019) Energy efficient algorithms in cloud computing: a green computing approach. Int J Adv Eng Technol 47–52

    Google Scholar 

  22. Tiwari PK, Joshi S (2016) A review on load balancing of virtual machine resources in cloud computing. In: Proceedings of first international conference on information and communication technology for intelligent systems, vol 2. Springer, Cham, pp 369–378

    Google Scholar 

  23. Tiwari PK, Joshi S (2018) Effective management of data centers resources for load balancing in cloud computing. Int J Inf Retr Res (IJIRR) 8(2):40–56

    Google Scholar 

  24. Sisodia PS, Tiwari V, Dahiya AK (2015) Measuring and monitoring urban sprawl of Jaipur city using remote sensing and GIS. Int J Inf Syst Soc Change (IJISSC) 6.2:46–65

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sharma, C., Tiwari, P.K., Agarwal, G. (2022). An Empirical Study of Different Techniques for the Improvement of Quality of Service in Cloud Computing. In: Nanda, P., Verma, V.K., Srivastava, S., Gupta, R.K., Mazumdar, A.P. (eds) Data Engineering for Smart Systems. Lecture Notes in Networks and Systems, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2641-8_32

Download citation

Publish with us

Policies and ethics