Skip to main content

Advertisement

SpringerLink
Log in

Task Deadline-Aware Energy-Efficient Scheduling Model for a Virtualized Cloud

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Data centers in cloud environment consume high amount of energy which not only raises the electricity bills of the data center hosting organizations but also has the strong environmental footprints. Therefore, energy efficiency of the data centers has become an important research issue. Many energy efficiency approaches have been proposed in the literature for cloud. Efficient resource scheduling is one of the important approaches to achieve energy efficiency in cloud. In this paper, a task deadline-aware energy-efficient scheduling model for virtualized cloud is presented. Independent and dynamically arriving deadline-aware tasks are scheduled by virtualizing the physical hosts in the data center. The proposed scheduling model at the first instance achieves the energy efficiency by executing maximum workload in the operational state of the host and at the second instance by maximum energy saving in the idle state of the host. In the operational state of the host, maximum workload is executed by exploiting the task slack time in a new context, and in the idle state of the host, maximum energy is saved by deploying core-level granularity of dynamic voltage and frequency scaling. The presented scheduling model is evaluated on the synthetic and real-world workload. Results clearly indicate that the presented scheduling model outperforms the existing scheduling model on the account of performance parameters of guarantee ratio, total energy consumption, energy consumption per task and resource utilization.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. Garg, S.K.; Buyya, R.: Green cloud computing and environmental sustainability. In: Harnessing Green It, Chapter 16, pp. 315–339 (2012)

  2. Google uses about 900000 servers. http://www.datacenterknowledge.com/archives/2011/08/01/report-google-uses-about-900000-servers/

  3. Bianchini, R.; Rajamony, R.: Power and energy management for server systems. Computer 37(11), 68–76 (2004)

    Article  Google Scholar 

  4. Fan, X.; Weber, W.-D.; Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th Annual International Symposium on Computer Architecture—ISCA ’07, vol. 35(2), pp. 13–23 (2007)

  5. Zhu, X.; Yang, L.T.; Chen, H.; Wang, J.; Yin, S.; Liu, X.: Real-time tasks oriented energy-aware scheduling in virtualized clouds. IEEE Trans. Cloud Comput. 2(2), 168–180 (2014)

    Article  Google Scholar 

  6. Barroso, L.A.; Holzle, U.: The case for energy-proportional computing. Computer 40(12), 33–37 (2007)

    Article  Google Scholar 

  7. Tomas, L.; Johan T.: Improving cloud infrastructure utilization through overbooking. In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference on—CAC ’13, Article no. 5 (2013)

  8. Erl, T.; Mahmood, Z.; Puttini, R.: Cloud Computing: Concepts, Technology, & Architecture. Prentice Hall, Englewood Cliffs (2013)

    Google Scholar 

  9. Shahzad, K.; Umer, A.I.; Nazir, B.: Reduce VM migration in bandwidth oversubscribed cloud data centres. In: 2015 IEEE 12th International Conference on Networking, Sensing and Control, pp. 140–145 (2015)

  10. Fly by wire. http://www.tejas.gov.in/technology/fly_by_wire.html

  11. Zhu, D.; Melhem, R.; Childers, B.: Scheduling with dynamic voltage/speed adjustment using slack reclamation in multiprocessor real-time systems. IEEE Trans. Parallel Distrib. Syst. 14(7), 686–700 (2003)

    Article  Google Scholar 

  12. Ejlali, A.; Al-Hashimi, B.M.; Eles, P.: Low-energy standby-sparing for hard real-time systems. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 31(3), 329–342 (2012)

    Article  Google Scholar 

  13. Neil, V.; Goossens, J.; Devillers, R.; Milojevic, D.; Navet, N.: Power-aware real-time scheduling upon identical multiprocessor platforms. In: 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, pp. 209–216 (2008)

  14. Haque, M.A; Aydin, H.; Zhu, D.: Energy-aware standby-sparing technique for periodic real-time applications. In: 2011 IEEE 29th International Conference on Computer Design (ICCD), pp. 190–197 (2011)

  15. Reddy, S.P.; Chandan, H.K.S.: Energy aware scheduling of real-time and non-real-time tasks on servers: (extensible to embedded systems). In: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), pp. 1–5 (2014)

  16. Reddy, S.P.; Chandan, H.K.S.: Energy aware scheduling of real-time and non-real-time tasks on cloud processors (green cloud computing). In: International Conference on Information Communication and Embedded Systems (ICICES2014), pp. 1–5 (2014)

  17. Zhu, X.; He, C.; Li, K.; Qin, X.: Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters. J. Parallel Distrib. Comput. 72(6), 751–763 (2012)

    Article  Google Scholar 

  18. Tesfatsion, S.K.; Wadbro, E.; Tordsson, J.: A combined frequency scaling and application elasticity approach for energy-efficient cloud computing. Sustain. Comput. Inf. Syst. 4(4), 205–214 (2014)

    Google Scholar 

  19. Ding, Y.; Qin, X.; Liu, L.; Wang, T.: Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Gener. Comput. Syst. 50, 62–74 (2015)

    Article  Google Scholar 

  20. Wu, C.-M.; Chang, R.-S.; Chan, H.-Y.: A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Gener. Comput. Syst. 37, 141–147 (2014)

    Article  Google Scholar 

  21. Isci, C.; Buyuktosunoglu, A.; Cher, C.-Y.; Bose, P.; Martonosi, M.: An analysis of efficient multi-core global power management policies: maximizing performance for a given power budget. In: 2006 39th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’06), pp. 347–358 (2006)

  22. Kim, W.; Gupta, M.S.; Wei, G.-Y.; Brooks, D.: System level analysis of fast, per-core DVFS using on-chip switching regulators. In: 2008 IEEE 14th International Symposium on High Performance Computer Architecture, pp. 123–134 (2008)

  23. Bergamaschi, R.; Han, G., et al.: Exploring power management in multi-core systems. In: 2008 Asia and South Pacific Design Automation Conference, pp. 708–713 (2008)

  24. Rangan, K.K.; Wei, G.-Y.; Brooks, D.: Thread motion: fine-grained power management for multi-core systems. In: Proceedings of the 36th Annual International Symposium on Computer Architecture—ISCA ’09, vol. 37(3), pp. 302–313 (2009)

  25. Rahmani, A.-M.; Haghbayan, M.-H.; Kanduri, A.; Weldezion, A.Y.; Liljeberg, P.; Plosila, J.; Jantsch, A.; Tenhunen, H.: Dynamic power management for many-core platforms in the dark silicon era: A multi-objective control approach. In: 2015 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), pp. 219–224 (2015)

  26. Calheiros, R.N.; Ranjan, R.; Beloglazov, A.; Fonticielha De Rose, C.A.; Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Multi-core processor. http://www.intel.in/content/www/in/en/processors/xeon/xeon-processor-e7-family.html

  29. Multi-core processor. http://www.amd.com/en-gb/innovations/software-technologies/multicore

  30. Google cluster data. https://github.com/google/cluster-data/blob/master/ClusterData2011_2.md

  31. Reiss, C.; Wilkes, J.; Hellerstein, J.: Google cluster-usage traces: format + schema. White paper, Google Inc. (2011)

Download references

Author information

Authors and Affiliations

  1. Sant Longowal Institute of Engineering and Technology, Sangrur, India

    Neha Garg & Major Singh Goraya

Authors
  1. Neha Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Major Singh Goraya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neha Garg.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garg, N., Goraya, M.S. Task Deadline-Aware Energy-Efficient Scheduling Model for a Virtualized Cloud. Arab J Sci Eng 43, 829–841 (2018). https://doi.org/10.1007/s13369-017-2779-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-017-2779-5

Keywords

Search

Navigation