Skip to main content

Advertisement

Log in

Penalty migration as a performance signaling method in energy-efficient clouds

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

Abstract

The concept of energy efficiency in clouds has gradually evolved and now includes not only traditional CPU and memory utilizations but also network traffic, number of migrations, etc. Recent literature also shows that the most energy efficient migration schedule is one that, in addition to minimizing migration count, also manages to vacate the most physical machines, thus allowing them to be powered down. While it is obvious that such migration schedules depend on frequent changes in VM-to-PM mapping layouts, existing literature does not offer any practical strategies to that effect. This paper retains the basic notion that lowering the number of migrations and powering down PMs contribute to a level of energy efficiency, but also proposes a new notion called penalty migration. In this paper, the Service and the Cloud have an SLA that spells out an effective range of operations for the Service’s applications, which, if the range is exceeded, can be penalized by the Cloud in several ways discussed in this paper. In addition to higher fluidity, the proposed strategies also improve QoS from the viewpoint of the Cloud’s resources as well as individual service populations. In turn, services can benefit from penalty migrations, treating migration events as QoS monitoring data. This proposal is the first known attempt to distribute the task of performance management between cloud and service providers, where penalty migrations are a form of signaling between the two roles.

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

Access this article

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

Similar content being viewed by others

References

  1. Zhanikeev M (2014) Optimizing virtual machine migration for energy-efficient clouds. IEICE Trans Commun E97-B(2):450–458

    Article  Google Scholar 

  2. Zhanikeev M (2016) Performance management of cloud populations via cloud probing. IPSJ J Inf Process 24 (1):99–108

    Google Scholar 

  3. Zhanikeev M (2014) Multi-Source Stream aggregation in the cloud. Book on advanced content delivery, streaming, and cloud services. Wiley

  4. Zhanikeev M (2014) A software design and algorithms for multicore capture in data center forensics. In: 9th ACM symposium on information, computer, and communication security workshops (ASIACCS/SFCS), pp 11–18

  5. Zhanikeev M (2014) A holistic community-based architecture for measuring end-to-end QoS at data centres. In: Inderscience international journal of computational science and engineering (IJCSE) (in print)

  6. Zhanikeev M, Tanaka Y (2012) Popularity-Based Modeling of flash events in synthetic packet traces. IEICE Tech Report Commun Qual 112(288):1–6

    Google Scholar 

  7. Duy T, Sato Y, Inoguchi Y (2011) A prediction-based green scheduler for datacenters in clouds. IEICE Trans Inf Syst E94-D(9):1731–1741

    Article  Google Scholar 

  8. Khanna G, Beaty K, Kar G, Kochut A (2006) Application performance management in virtualized server environments. In: Network operations and management symposium (NOMS), pp 373–381

  9. Dhiman G, Marchetti G, Rosing T (2009) vGreen: a system for energy efficient computing in virtualized environments. In: 14th ACM/IEEE international symposium on low power electronics and design, pp 243–248

  10. Xu J, Fortes J (2010) Multi-objective virtual machine placement in virtualized data center environments. In: IEEE/ACM International conference on green computing and communications (GreenCom) jointly with conference on cyber, physical and social computing (CPSCom), pp 179–188

  11. Stage A, Setzer T (2009) Network-aware migration control and scheduling of differentiated virtual machine workloads. In: CLOUD, pp 9–14

  12. Beloglazov A, Buyya R (2010) Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In: 8th International workshop on middleware for grids, clouds, and e-science, Article no. 4

  13. Takahashi S, Takefusa A, Shigeno M, Nakada H, Kudoh T, Yoshise A (2012) Virtual machine packing algorithms for lower power consumption. In: 4th International conference on cloud computing technology and science (CloudCom), pp 161– 168

  14. Andreolini M, Casolari S, Colajanni M, Messori M (2009) Dynamic load management of virtual machines in a cloud architecture. In: ICST CLOUDCOMP, pp 201–214

  15. Chandra A, Gong W, Shenoy P (2003) Dynamic resource allocation for shared data centers using online measurements. In: International workshop on QoS (IWQos)

  16. Fan X, Weber W, Barroso L (2007) Power provisioning for a warehouse-sized computer. In: 34th Annual international symposium on computer architecture (ISCA), pp 13–23

  17. Voorsluys W, Broberg J, Venugopal S, Buyya R (2009) Cost of virtual machine live migration in clouds: a performance evaluation. In: CloudCom, pp 254–265

  18. Wood T, Shenoy P, Venkataramani A, Yousif M (2007) Black-box and gray-box strategies for virtual machine migration. In: 4th USENIX Symp. on networked systems design and implementation, pp 229–242

  19. Antonio C, Tusa F, Villari M, Puliofito A (2010) Improving virtual machine migration in federated cloud environments. In: Second international conference on evolving internet, pp 61– 67

  20. Xiong P, Wang Zh, Jung G, Pu C (2010) Study on performance management and application behavior in virtualized environment. In: IEEE/IFIP Network operations and management symposium (NOMS), pp 841–844

  21. Lu J, Turner J (2006) Efficient mapping of virtual networks onto a shared substrate. Technical Report no.WUSCE-2006-35, Washington University in St. Louis

  22. Chekuri C, Khanna S (1999) On multidimensional bin packing problems. In: The 10th ACM symposium on discrete algorithms, pp 185–194

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marat Zhanikeev.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhanikeev, M. Penalty migration as a performance signaling method in energy-efficient clouds. Ann. Telecommun. 72, 401–413 (2017). https://doi.org/10.1007/s12243-017-0577-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-017-0577-4

Keywords

Navigation