Skip to main content

Advertisement

SpringerLink
Log in

SEATS: smart energy-aware task scheduling in real-time cloud computing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Mitigating energy consumption in Clouds reduces operational costs for providers. Power management policies which aim to reduce total energy consumed in data-centers pose challenges in both hardware technologies and resource management policies. We introduce an optimal utilization level of a host to execute a certain number of instructions to minimize energy consumption of the host. We also propose a virtual machine (VM) scheduling algorithm based on the unsurpassed utilization level to come up with optimal energy consumption while meeting a given QoS. In other words, our proposed algorithm regulates allocated computing resources of VMs on a host which results in reaching an optimal energy level in the host. The simulation results show that our proposed method not only reduces total energy consumption of a Cloud by 60 %, but also has a profound impact on turnaround times of real-time tasks by 94 %. It also increases the acceptance rate of arrival tasks by 96 %. Moreover, it takes a leading part in accepting lengthy tasks which have short deadlines.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. AMD: Mobile amd duron processor model 7 data sheet. http://www.ic72.com/pdf_file/a/305095.pdf

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

    Article  Google Scholar 

  3. Beloglazov A, Buyya R (2010) Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In: Proceedings of the 8th international workshop on middleware for grids, clouds and e-Science, p 4. ACM

  4. 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. IEEE Computer Society, pp 826–831

  5. Beloglazov A, Buyya R (2011) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience 24(13):1397–1420

  6. Borgettoa D, Casanovab H, Da Costaa G, Piersona J (2012) Energy-aware service allocation. Future Gener Comput Syst 28(5):769–779

  7. Brown R et al (2008) Report to congress on server and data center energy efficiency: public law, break. http://www.energystar.govia/...EPA_Datacenter_Report_Congress_Final1.pdf

  8. Buyya R, Ranjan R, Calheiros R (2009) Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: challenges and opportunities. In: International conference on high performance computing and simulation, 2009. HPCS’09. IEEE, pp. 1–11

  9. Calheiros R, Ranjan R, Beloglazov A, De Rose C, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50

  10. Chen J, Kuo C (2007) Energy-efficient scheduling for real-time systems on dynamic voltage scaling (dvs) platforms. In: 13th IEEE international conference on embedded and real-time computing systems and applications, 2007. RTCSA 2007. IEEE, pp 28–38

  11. Choi K, Lee W, Soma R, Pedram M (2004) Dynamic voltage and frequency scaling under a precise energy model considering variable and fixed components of the system power dissipation. In: Proceedings of the 2004 IEEE/ACM International conference on computer-aided design. IEEE Computer Society, pp 29–34

  12. Elnozahy E, Kistler M, Rajamony R (2003) Energy-efficient server clusters. In: 2nd international workshop on power aware computing systems (PACS), pp 179–197

  13. Fallenbeck N, Picht H, Smith M, Freisleben B (2006) Xen and the art of cluster scheduling. In: First international workshop on virtualization technology in distributed computing, 2006. VTDC 2006. IEEE, p 4

  14. Fan X, Weber W, Barroso L (2007) Power provisioning for a warehouse-sized computer. ACM SIGARCH Comput Archit News 35(2):13–23

    Article  Google Scholar 

  15. Garg SK, Yeo CS, Anandasivam A, Buyya R (2011) Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. J Parallel Distrib Comput 71(6):732–749

    Article  MATH  Google Scholar 

  16. Gochman S, Ronen R, Anati I, Berkovits A, Kurts T, Naveh A, Saeed A, Sperber Z, Valentine R (2003) The intel pentium m processor: microarchitecture and performance. Intel Technol J 7(2):21–36

    Google Scholar 

  17. Goiri Í, Berral JL, Fitó JO, Julià F, Nou R, Guitart J, Gavaldà R, Torres J (2012) Energy-efficient and multifaceted resource management for profit-driven virtualized data centers. Future Gener Comput Syst 28(5):718–731

    Article  Google Scholar 

  18. Goiri I, Guitart J, Torres J (2010) Characterizing cloud federation for enhancing providers’ profit. In: 2010 IEEE 3rd international conference on cloud computing (CLOUD). IEEE, pp 123–130

  19. Goudarzi H, Ghasemazar M, Pedram M (2012) Sla-based optimization of power and migration cost in cloud computing. In: 2012 12th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid). IEEE, pp 172–179

  20. Jejurikar R, Pereira C, Gupta R (2004) Leakage aware dynamic voltage scaling for real-time embedded systems. In: Design automation conference. ACM/IEEE, pp 275–280

  21. Kim K, 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. ACM, p 1

  22. Kim K, Beloglazov A, Buyya R (2011) Power-aware provisioning of virtual machines for real-time cloud services. Concurrency and Computation: Practice and Experience 23(13):1491–1505

  23. Kim K, Buyya R, Kim J (2007) Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clusters. In: Proceedings of the seventh IEEE international symposium on cluster computing and the grid. IEEE Computer Society, pp 541–548

  24. Kim K, Lee W, Jong K, Buyya R (2010) Sla-based scheduling of bag-of-tasks applications on power-aware cluster systems. IEICE Trans Inf Syst 93(12):3194–3201

    Article  Google Scholar 

  25. Kuroda T, Suzuki K, Mita S, Fujita T, Yamane F, Sano F, Chiba A, Watanabe Y, Matsuda K, Maeda T et al (1998) Variable supply-voltage scheme for low-power high-speed CMOS digital design. IEEE J Solid State Circuits 33(3):454–462

    Article  Google Scholar 

  26. Lee Y, Zomaya A (2012) Energy efficient utilization of resources in cloud computing systems. J Supercomput 60(2):268–280

    Article  MathSciNet  Google Scholar 

  27. Li B, Li J, Huai J, Wo T, Li Q, Zhong L (2009) Enacloud: an energy-saving application live placement approach for cloud computing environments. In: IEEE international conference on cloud computing, 2009. CLOUD’09. IEEE, pp. 17–24

  28. Niu L, Quan G (2004) Reducing both dynamic and leakage energy consumption for hard real-time systems. In: Proceedings of the 2004 international conference on compilers, architecture, and synthesis for embedded systems. ACM, pp 140–148

  29. Pedram M (2012) Energy-efficient datacenters. IEEE Trans Comput Aided Des Integr Circuits Syst 31(10):1465–1484

    Article  Google Scholar 

  30. Pinheiro E, Bianchini R, Carrera E, Heath T (2001) Load balancing and unbalancing for power and performance in cluster-based systems. In: Workshop on compilers and operating systems for low power, vol 180, pp 182–195

  31. Rodero I, Lee E, Pompili D, Parashar M, Gamell M, Figueiredo R (2010) Towards energy-efficient reactive thermal management in instrumented datacenters. In: 2010 11th IEEE/ACM international conference on grid computing (GRID). IEEE, pp 321–328

  32. Semeraro G, Magklis G, Balasubramonian R, Albonesi D, Dwarkadas S, Scott M (2002) Energy-efficient processor design using multiple clock domains with dynamic voltage and frequency scaling. In: Eighth international symposium on high-performance computer architecture, 2002. Proceedings. IEEE, pp 29–40

  33. Smith J, Nair R (2005) Virtual machines: versatile platforms for systems and processes. Morgan Kaufmann, San Francisco

  34. Sotomayor B, Montero R, Llorente I, Foster I (2009) Virtual infrastructure management in private and hybrid clouds. Internet Comput IEEE 13(5):14–22

    Article  Google Scholar 

  35. Spellmann A, Gimarc R, Preston M, Hyperformix O (2009) Leveraging the cloud for green it: predicting the energy, cost and performance of cloud computing. In: Computer measurement group conference (CMG 09), Dallas, Texas

  36. Srikantaiah S, Kansal A, Zhao F (2008) Energy aware consolidation for cloud computing. In: Proceedings of the 2008 conference on power aware computing and systems. USENIX Association, p 10

  37. Tomlinson B, Silberman M, White J (2011) Can more efficient it be worse for the environment. Computer 44(1):87–89

    Article  Google Scholar 

  38. Wang L, Kunze M, Tao J (2008) Performance evaluation of virtual machine-based grid workflow system. Concurr Comput Pract Exp 20(15):1759–1771

    Article  Google Scholar 

  39. Wang L, von Laszewski G, Dayal J, Wang F (2010) Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with dvfs. In: 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing (CCGrid). IEEE, pp 368–377

  40. Wang L, Lu Y (2008) Efficient power management of heterogeneous soft real-time clusters. In: Real-time systems symposium, 2008. IEEE, pp 323–332

  41. Weiser M, Welch B, Demers A, Shenker S (1996) Scheduling for reduced cpu energy. In: Mobile Computing. The Kluwer international series in engineering and computer science, vol 353, pp 449–471

  42. Yao F, Demers A, Shenker S (1995) A scheduling model for reduced cpu energy. In: 36th Annual symposium on foundations of computer science, 1995. Proceedings. IEEE, pp 374–382

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Engineering, and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

    Seyedmehdi Hosseinimotlagh, Farshad Khunjush & Rashidaldin Samadzadeh

Authors
  1. Seyedmehdi Hosseinimotlagh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farshad Khunjush
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rashidaldin Samadzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farshad Khunjush.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hosseinimotlagh, S., Khunjush, F. & Samadzadeh, R. SEATS: smart energy-aware task scheduling in real-time cloud computing. J Supercomput 71, 45–66 (2015). https://doi.org/10.1007/s11227-014-1276-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-014-1276-9

Keywords

Search

Navigation