, Volume 98, Issue 9, pp 949–963 | Cite as

Green Service Level Agreement (GSLA) framework for cloud computing



As the organizations are shifting their workload on cloud computing, the demand of cloud services has increased tremendously. With the increased usage of cloud data centers, there is huge consumption of energy (power and heat), contributing to high operational costs and carbon footprints to the environment. So far, research has been carried out to optimize energy usage for cloud resources. However, most of the work on energy optimization is centered on the operational phase of a data center. This paper focuses on energy reduction at Service Level Agreement (SLA) level. Cloud resources are provisioned with Green SLA aware cloud resource reservation (GSLACRR) algorithm. This work proposes Green Service Level Agreement (GSLA) template and negotiation strategies for cloud services. It offers cloud resource services in an energy efficient manner to the users.


Service Level Agreement Resource provisioning Energy efficient cloud Energy-aware VM placement Virtual machine 

Mathematics Subject Classification

68N01 68U01 68M01 68M14 62H15 


  1. 1.
    Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gener Comput Syst 28(5):755–768CrossRefGoogle Scholar
  2. 2.
    Minh QD, Federico M, Domenico S, Giafreda R (2012) T-Alloc A practical energy efficient resource allocation algorithm for traditional data centers. Futur Gener Comput Syst 28(5):791–800CrossRefGoogle Scholar
  3. 3.
    Lee YC, Zomaya AY (2012) Energy efficient utilization of resources in cloud computing systems. J Supercomput 60(2):268–280MathSciNetCrossRefGoogle Scholar
  4. 4.
    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. Concurr Comput Pract Exp 24(13):1397–1420CrossRefGoogle Scholar
  5. 5.
    Rizvandi NB, Taheri J, Zomaya AY (2011) Some observations on optimal frequency selection in DVFS-based energy consumption minimization. J Parallel Distrib Comput 71(8):1154–1164CrossRefMATHGoogle Scholar
  6. 6.
    Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing, pp 826–831Google Scholar
  7. 7.
    Kim KH, Beloglazov A, Buyya R (2009) Power-aware provisioning of cloud resources for real-time services. In: Proceedings of the 7th international workshop on middleware for grids, clouds and e-science, p 1Google Scholar
  8. 8.
    Kan EY, Chan WK, Tse TH (2012) EClass: An execution classification approach to improving the energy-efficiency of software via machine learning. J Syst Softw 85(4):960–973CrossRefGoogle Scholar
  9. 9.
    Guzek M, Diaz CO, Pecero JE, Bouvry P, Zomaya AY (2012) Impact of Voltage Levels Number for Energy-aware Bi-objective DAG Scheduling for Multi-processors Systems. Advances in Information Technology. Springer, Berlin, pp 70–80Google Scholar
  10. 10.
    Sharma RK, Bash CE, Patel CD, Friedrich RJ, Chase JS (2005) Balance of power: Dynamic thermal management for internet data centers. Internet Comput IEEE 9(1):42–49CrossRefGoogle Scholar
  11. 11.
    Moore JD, Chase JS, Ranganathan P, Sharma RK (2005) Making Scheduling Cool: Temperature-Aware Workload Placement in Data Centers. In USENIX annual technical conference, General Track, pp 61–75Google Scholar
  12. 12.
    Chaudhry MT, Ling TC, Manzoor A, Hussain SA, Kim J (2015) Thermal-aware scheduling in green data centers. ACM Comput Surv (CSUR) 47(3):39CrossRefGoogle Scholar
  13. 13.
    Haque ME, Le K, Goiri Í, Bianchini R, Nguyen TD (2013) Providing Green SLAs in High Performance Computing Clouds. In: IEEE international Green computing conference (IGCC). IEEE, Arlington, pp 1–11Google Scholar
  14. 14.
    Bunse C, Klingert S, Schulze T (2012) GreenSLAs: Supporting energy-efficiency through contracts. Energy Efficient Data Centers. Springer, Berlin, pp 54–68Google Scholar
  15. 15.
    von Laszewski G, Wang L (2010) GreenIT service level agreements. In: Grids and Service-Oriented Architectures for Service Level Agreements Springer, US, pp 77–88Google Scholar
  16. 16.
    Dupont C, Giuliani G, Hermenier F, Schulze T, Somov A (2012) An energy aware framework for virtual machine placement in cloud federated data centres. In: IEEE third international conference on future energy systems: where energy, computing and communication meet (e-Energy), pp 1–10Google Scholar
  17. 17.
    Copil G, Moldovan D, Salomie I, Cioara T, Anghel I, Borza D (2012) Cloud SLA negotiation for energy saving—a particle swarm optimization approach. In: IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp 289–296Google Scholar
  18. 18.
    Rasheed H, Rumpl A, Wäldrich O, Ziegler W (2012) A standards-based approach for negotiating service QoS with cloud infrastructure providers. In: eChallenges ConferenceGoogle Scholar
  19. 19.
    Gao Y, Guan H, Qi Z, Wang B, Liu L (2013) Quality of service aware power management for virtualized data centers. J Syst Arch 59(4):245–259CrossRefGoogle Scholar
  20. 20.
    Chen X, Li K, Liu C, Li K (2014) SLA-based energy aware scheduling of precedence-constrained applications on DVFS-enabled clusters. In: 20th IEEE international conference on parallel and distributed systems (ICPADS). IEEE, HsinchuGoogle Scholar
  21. 21.
    Basmadjian R, Bunse C, Georgiadou V, Giuliani G, Klingert S, Lovasz G, Majanen M (2010) Fit4green-energy aware ICT optimization policies. In: Proceedings of the COST Action IC0804 on energy efficiency in large scale distributed systems—1st year, pp 88–92Google Scholar
  22. 22.
    Dupont Corentin et al (2015) Plug4Green: a flexible energy-aware VM manager to fit data centre particularities. Ad Hoc Netw 25:505–519CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Deng N, Stewart C, Gmach D, Arlitt M, Kelley J (2012) Adaptive green hosting. In: ACM Proceedings of the 9th international conference on Autonomic computing, pp 135–144Google Scholar
  25. 25.
    Oró E, Depoorter V, Garcia A, Salom J (2015) Energy efficiency and renewable energy integration in data centres. Strategies and modelling review. Renew Sustain Energy Rev 42:429–445. doi: 10.1016/j.rser.2014.10.035 CrossRefGoogle Scholar
  26. 26.
    Li C, Hu Y, Zhou R, Liu M, Liu L, Yuan J, Li T (2013) Enabling datacenter servers to scale out economically and sustainably. In: Proceedings of the 46th annual IEEE/ACM international symposium on microarchitecture, pp 322–333Google Scholar
  27. 27.
    Li C, Zhou R, Li T (2013) Enabling distributed generation powered sustainable high-performance data center. In: IEEE proceeding of the 19th international symposium on high performance computer architecture (HPCA2013), pp 35-46Google Scholar
  28. 28.
    Deng N, Stewart C, Li J (2011) Concentrating renewable energy in grid-tied datacenters. In: IEEE proceeding of international symposium on sustainable systems and technology (ISSST), pp 1–6Google Scholar
  29. 29.
    Orgerie AC (2011) An energy-efficient reservation framework for large-scale distributed systems. PhD thesis, Ecole Normale Supérieure de Lyon–FranceGoogle Scholar
  30. 30.
    Goyal S, Bawa S, Singh B (2015) Energy optimized resource scheduling algorithm for private cloud computing. International Journal of AdHoc and Ubiquitous Computing, Inderscience (In Press, Accepted Manuscript) Google Scholar
  31. 31.
    Kernel-based Virtual Machine:
  32. 32.
    Ubuntu operating system.
  33. 33.
  34. 34.
  35. 35.
  36. 36.
    Gavrichenkov I (2014) Power consumption of intelCore i5 processor.

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThapar UniversityPatialaIndia
  2. 2.Microsoft India (R & D) Ltd.HyderabadIndia

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