Power conserving resource allocation scheme with improved QoS to promote green cloud computing

Abstract

Though cloud computing has grabbed the attention of several industrialists and educationalists, the only concern of cloud computing is the uncontrollable rise of cloud data centres. The improper utilization of cloud resources paves way for inefficiency and environmental hazard as well. Understanding the seriousness of this issue, several researchers contribute to promote green cloud computing through different ways. The Green Cloud Computing is the act of executing approaches and the techniques to improve proficiency of the figuring assets so as to decrease the vitality utilization and natural effect of their usage. The power consumption of the data centre offers the features like web based checking, live virtual machine movement, and advancement of Virtual Machine Placement. This work focuses on effective resource allocation scheme for cloud users, which does not compromise on QoS by employing two layers such as Cloud Manager Layer (CMLs) and Green Manager Layer (GML). The CML is responsible for choosing the suitable resources out of all available resources and the GML picks the best one out of it. Due to this optimal selection of resource, the average service response time is reduced at the cost of minimized power consumption. When handling 500 service requests, the proposed work consumes about 4298 W and the comparative approaches consume more power.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Beik R (2012) Green cloud computing: an energy-aware layer in software architecture. In: 2012 Spring congress on engineering and technology, pp 1–4

  2. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Fut Gener Comput Syst 28(5):755–768

    Google Scholar 

  3. Bruneo D, Lhoas A, Longo F, Puliafito A (2014) Modeling and evaluation of energy policies in green clouds. IEEE Trans Parallel Distrib Syst 26(11):3052–3065

    Google Scholar 

  4. Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308

  5. Chiang YJ, Ouyang YC, Hsu CHR (2014) An efficient green control algorithm in cloud computing for cost optimization. IEEE Trans Cloud Comput 3(2):145–155

    Google Scholar 

  6. Dehkordi ZG, Shirazi HA (2014) The strategic plan founded on efficiency of using green computing techniques in data centers. Compusoft 3(8):1038

    Google Scholar 

  7. Duy TVT, Sato Y, Inoguchi Y (2010). Performance evaluation of a green scheduling algorithm for energy savings in cloud computing. In: 2010 IEEE international symposium on parallel & distributed processing, workshops and Phd forum (IPDPSW), pp 1–8

  8. Fan Q, Ansari N, Sun X (2017) Energy driven avatar migration in green cloudlet networks. IEEE Commun Lett 21(7):1601–1604

    Google Scholar 

  9. Farahnakian F, Ashraf A, Pahikkala T, Liljeberg P, Plosila J, Porres I, Tenhunen H (2014) Using ant colony system to consolidate VMs for green cloud computing. IEEE Trans Serv Comput 8(2):187–198

    Google Scholar 

  10. Garg SK, Buyya R (2012) Green cloud computing and environmental sustainability. Harnessing Green IT Princ Pract 2012:315–340

    Google Scholar 

  11. Geetha P, Robin CR (2017) A comparative-study of load-cloud balancing algorithms in cloud environments. In: 2017 international conference on energy, communication, data analytics and soft computing, pp 806–810

  12. Geetha P, Robin CR (2019) A Performance—Review of Cloud Scheduling Algorithms into Green Environments 1(3):1160–1167

  13. Geetha P, Robin CR (2019) SAMR: optimal workflow of VMs in cloud computing. In: International conference on recent trends in computing, communication and networking technologies (ICRTCCNT’19), pp 1–8

  14. Hasan MS, Kouki Y, Ledoux T, Pazat JL (2015) Exploiting renewable sources: when Green SLA becomes a possible reality in Cloud computing. IEEE Trans Cloud Comput 5(2):249–262

    Google Scholar 

  15. Jain A, Mishra M, Peddoju SK, Jain N (2013) Energy efficient computing-green cloud computing. In: 2013 International conference on energy efficient technologies for sustainability. IEEE, pp 978–982

  16. John J (2014) Green computing strategies for improving energy efficiency in it systems. Int J Sci Eng Technol 3(6):715–717

    Google Scholar 

  17. Jyoti A, Shrimali M, Tiwari S et al (2020) Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01747-z

    Article  Google Scholar 

  18. Khosravi A, Andrew LL, Buyya R (2017) Dynamic VM placement method for minimizing energy and carbon cost in geographically distributed cloud data centers. IEEE Trans Sustain Comput 2(2):183–196

    Google Scholar 

  19. Kumar V, Kiruthiga P (2014) Green computing–an eco friendly approach for energy efficiency and minimizing E-waste. Int J Eng Res 3(5):356–359

    Google Scholar 

  20. Larumbe F, Sanso B (2013) A tabu search algorithm for the location of data centers and software components in green cloud computing networks. IEEE Trans Cloud Comput 1:22–35

    Google Scholar 

  21. Liu F, Shu P, Jin H, Ding L, Yu J, Niu D, Li B (2013) Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications. IEEE Wirel Commun 20(3):14–22

    Google Scholar 

  22. Mastelic T, Brandic I (2015) Recent trends in energy-efficient cloud computing. IEEE Cloud Comput 2(1):40–47

    Google Scholar 

  23. Mastelic T, Oleksiak A, Claussen H, Brandic I, Pierson JM, Vasilakos AV (2015) Cloud computing: survey on energy efficiency. Acm Comput Surv (csur) 47(2):33

    Google Scholar 

  24. Mccomb K, Pusey A, Packer C, Grinnell J (1993) Female lions can identify potentially infanticidal males from their roars. Proc R Soc Lond Ser B Biol Sci 252(1333):59–64

    Google Scholar 

  25. Mishra S, Sangaiah AK, Sahoo MN et al (2019) Pareto-optimal cost optimization for large scale cloud systems using joint allocation of resources. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01601-x

    Article  Google Scholar 

  26. Nan Y, Li W, Bao W, Delicato FC, Pires PF, Dou Y, Zomaya AY (2017) Adaptive energy-aware computation offloading for cloud of things systems. IEEE Access 5:23947–23957

    Google Scholar 

  27. Nguyen KK, Cheriet M (2014) Environment-aware virtual slice provisioning in green cloud environment. IEEE Trans Serv Comput 8(3):507–519

    Google Scholar 

  28. Praveenchandar J, Tamilarasi A (2020) Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01794-6

    Article  Google Scholar 

  29. Priya B, Pilli ES, Joshi RC (2013) A survey on energy and power consumption models for greener cloud. In: 2013 3rd IEEE international advance computing conference (IACC). IEEE, pp 76–82

  30. Qiu C, Shen H, Chen L (2018) Towards green cloud computing: demand allocation and pricing policies for cloud service brokerage. IEEE Trans Big Data 5(2):238–251

    Google Scholar 

  31. Schaller GB (2009) The Serengeti lion: a study of predator-prey relations. University of Chicago Press, Chicago

    Google Scholar 

  32. Siddiqui J (2013) Green computing: protect our environment from computer and its devices. Compusoft 2(12):410

    Google Scholar 

  33. Tiwari S (2011) Need of green computing measures for Indian IT industry. J Energy Technol Policy 1(4):18–24

    Google Scholar 

  34. Wadhwa B, Verma A (2014) Energy saving approaches for green cloud computing: a review. In: 2014 recent advances in engineering and computational sciences (RAECS). IEEE, pp 1–6

  35. Yang Y, Chang X, Liu J, Li L (2015) Towards robust green virtual cloud data center provisioning. IEEE Trans Cloud Comput 5(2):168–181

    Google Scholar 

  36. Yeganeh H, Salahi A, Pourmina MA (2019) A novel cost optimization method for mobile cloud computing by capacity planning of green data center with dynamic pricing. Can J Electr Comput Eng 42(1):41–51

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to P. Geetha.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Geetha, P., Robin, C.R.R. Power conserving resource allocation scheme with improved QoS to promote green cloud computing. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02384-2

Download citation

Keywords

  • Green cloud computing
  • CML
  • GML
  • Power consumption