Skip to main content

Advertisement

Log in

Virtual machine consolidation based on interference modeling

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

Abstract

Server consolidation is very attractive for cloud computing platforms to improve energy efficiency and resource utilization. Advances in multi-core processors and virtualization technologies have enabled many workloads to be consolidated in a physical server. However, current virtualization technologies do not ensure performance isolation among guest virtual machines, which results in degraded performance due to contention in shared resources along with violation of service level agreement (SLA) of the cloud service. In that sense, minimizing performance interference among co-located virtual machines is the key factor of successful server consolidation policy in the cloud computing platforms. In this work, we propose a performance model that considers interferences in the shared last-level cache and memory bus. Our performance interference model can estimate how much an application will hurt others and how much an application will suffer from others. We also present a virtual machine consolidation method called swim which is based on our interference model. Experimental results show that the average performance degradation ratio by swim is comparable to the optimal allocation.

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
Algorithm 1
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. The number of LLC references is the same as the number of L2 misses.

References

  1. Intel Core i7-950 Nehalem architecture processor. http://goo.gl/ZYvhO

  2. KVM (Kernel-based Virtual Machine). http://www.linux-kvm.org/

  3. VMware. http://www.vmware.com/

  4. Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Neugebauer R, Pratt I, Warfield A (2003) Xen and the art of virtualization. In: Proceedings of SOSP ’03, New York, NY, USA

    Google Scholar 

  5. Cho S, Jin L (2006) Managing distributed, shared l2 caches through os-level page allocation. In: Proceedings of the 39th annual IEEE/ACM international symposium on microarchitecture, MICRO ’39. IEEE Comput Soc, Washington, pp 455–468

    Google Scholar 

  6. Citrix: XenServer. http://www.citrix.com/

  7. David H, Fallin C, Gorbatov E, Hanebutte UR, Mutlu O (2011) Memory power management via dynamic voltage/frequency scaling. In: Proceedings of the 8th ACM international conference on autonomic computing, ICAC ’11. ACM, New York, pp 31–40

    Chapter  Google Scholar 

  8. Govindan S, Liu J, Kansal A, Sivasubramaniam A (2011) Cuanta: quantifying effects of shared on-chip resource interference for consolidated virtual machines. In: Proceedings of the 2nd ACM Symposium on Cloud Computing (SOCC)

    Google Scholar 

  9. Gupta D, Cherkasova L, Gardner R, Vahdat A (2006) Enforcing performance isolation across virtual machines in xen. In: Proceedings of the ACM/IFIP/USENIX 2006 international conference on middleware, Melbourne, Australia

    Google Scholar 

  10. Intel and VMware: Intelligent queueing technologies for virtualization. http://www.vmware.com/files/pdf/partners/intel/vmdq-white-paper-wp.pdf

  11. Intel Corporation: Single-chip Cloud Computer. http://techresearch.intel.com/ProjectDetails.aspx?Id=1

  12. Jaleel A, Borch E, Bhandaru M, Steely SC Jr., Emer J (2010) Achieving non-inclusive cache performance with inclusive caches: temporal locality aware (tla) cache management policies. In: Proceedings of the 2010 43rd annual IEEE/ACM international symposium on microarchitecture, MICRO ’43, pp 151–162

    Chapter  Google Scholar 

  13. Mars J, Tang L, Hundt R, Skadron K, Soffa ML (2011) Bubble-up: increasing sensible co-locations for improved utilization in modern warehouse scale computers. In: Proceedings of the 44th annual IEEE/ACM international symposium on microarchitecture (MICRO)

    Google Scholar 

  14. Microsoft: Hyper-V. http://www.microsoft.com/server-cloud/

  15. Mutlu O, Moscibroda T (2008) Parallelism-aware batch scheduling: enhancing both performance and fairness of shared dram systems. In: Proceedings of the 35th annual international symposium on computer architecture, ISCA ’08. IEEE Comput Soc, Washington, pp 63–74

    Google Scholar 

  16. Nesbit KJ, Laudon J, Smith JE (2007) Virtual private caches. In: Proceedings of the 34th annual international symposium on computer architecture, ISCA ’07. ACM, New York, pp 57–68

    Chapter  Google Scholar 

  17. Qureshi MK, Jaleel A, Patt YN, Steely SC, Emer J (2007) Adaptive insertion policies for high performance caching. In: Proceedings of the 34th annual international symposium on computer architecture, ISCA ’07, pp 381–391

    Chapter  Google Scholar 

  18. Qureshi MK, Patt YN (2006) Utility-based cache partitioning: a low-overhead, high-performance, runtime mechanism to partition shared caches. In: Proceedings of the 39th annual IEEE/ACM international symposium on microarchitecture, Orlando, FL, USA

    Google Scholar 

  19. Schuff DL, Kulkarni M, Pai VS (2010) Accelerating multicore reuse distance analysis with sampling and parallelization. In: Proceedings of the 19th international conference on parallel architectures and compilation techniques, PACT ’10, pp 53–64

    Chapter  Google Scholar 

  20. Smaragdakis Y, Kaplan S, Wilson P (1999) Eelru: simple and effective adaptive page replacement. In: Proceedings of the 1999 ACM SIGMETRICS international conference on measurement and modeling of computer systems, SIGMETRICS ’99, pp 122–133

    Chapter  Google Scholar 

  21. Standard Performance Evaluation Corporation: SPEC CPU2006. http://www.spec.org/cpu2006/

  22. Tam DK, Azimi R, Soares LB, Stumm M (2009) Rapidmrc: approximating l2 miss rate curves on commodity systems for online optimizations. In: Proceedings of the 14th international conference on architectural support for programming languages and operating systems, ASPLOS ’09. ACM, New York, pp 121–132

    Chapter  Google Scholar 

  23. Tilera Corporation: TILE-Gx Processor Family. http://www.tilera.com/products/processors/TILE-Gx_Family

  24. Zhang J, Sivasubramaniam A, Wang Q, Riska A, Riedel E (2006) Storage performance virtualization via throughput and latency control. Transf Storage 2:283–308

    Article  Google Scholar 

  25. Zhang X, Dwarkadas S, Shen K (2009) Towards practical page coloring-based multicore cache management. In: Proceedings of the 4th ACM European conference on computer systems (EuroSys)

    Google Scholar 

  26. Zhuravlev S, Blagodurov S, Fedorova A (2010) Addressing shared resource contention in multicore processors via scheduling. In: Proceedings of the fifteenth edition of ASPLOS on architectural support for programming languages and operating systems, ASPLOS ’10. ACM, New York, pp 129–142

    Chapter  Google Scholar 

Download references

Acknowledgements

This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology. (No. 2010-0020731) The ICT at Seoul National University provided research facilities for this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Heon Y. Yeom.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, Sg., Eom, H. & Yeom, H.Y. Virtual machine consolidation based on interference modeling. J Supercomput 66, 1489–1506 (2013). https://doi.org/10.1007/s11227-013-0939-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-013-0939-2

Keywords

Navigation