Skip to main content

Advertisement

Springer Nature Link
Log in

Models for availability and power consumption evaluation of a private cloud with VMM rejuvenation enabled by VM Live Migration

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

Abstract

Software aging affects the availability of Virtual Machine Monitor (VMM), one of the main components of virtualized environments. Software aging causes internal software degradation due to bugs activation and accumulation during its execution. Software rejuvenation implemented through Virtual Machine (VM) Live Migration may be applied to cope with software aging effects on cloud computing system. Another relevant problem is the power consumption of using software rejuvenation techniques in the virtualized environments. This paper presents availability models based on Stochastic Petri Nets to evaluate two VM Live Migration approaches. These approaches are based on the redundancy schemes: Warm-Standby and Cold-Standby. In the Cold-Standby migration, the migration target machine is started only before the VM Live Migration. In the Warm-Standby Migration, the migration target machine runs along with the source migration machine. Results show that VM Live Migration causes a significant improvement in system availability. Scenarios with a heavy workload present an annual downtime reduction of 164 h. The availability comparison between two approaches reveals that the Cold-Standby approach has a slightly better result due to the decrease in the total number of VM Live Migrations. The power consumption results show that Cold-Standby approach is more efficient in power consumption. In all the observed scenarios, the costs savings by using Cold-Standby approach exceed 40%. The highlights of this paper are: i) a comprehensive model for availability evaluation of cloud computing with VMM rejuvenation through VM migration scheduling; ii) sensitivity analysis to define proper rejuvenation scheduling to maximize system availability and iii) power consumption comparison of the two adopted migration approaches.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

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

Notes

  1. CS PA \(\rightarrow \) Cold-Standby Migration peak availability, WS PA \(\rightarrow \) Warm-Standby Migration peak availability.

  2. Dell Server PowerEdge 1850 with two Xeon 800FSB processors, 8 GB of RAM.

Abbreviations

CRAC:

Computer Room Air Conditioner

PM:

Physical machine

SPN:

Stochastic Petri Net

SRN:

Stochastic Reward Net

TTARF:

Time to Aging-Related Failure

UPS:

Uninterruptible Power Supply

VM:

Virtual Machine

VM:

Virtual Machine Monitor

References

  1. Araujo J, Matos R, Maciel P, Matias R, Beicker I (2011) Experimental evaluation of software aging effects on the eucalyptus cloud computing infrastructure. In: Proceedings of the Middleware 2011 Industry Track Workshop, p 4. ACM

  2. Araujo J, Matos R, Maciel P, Vieira F, Matias R, Trivedi K (2011) Software rejuvenation in eucalyptus cloud computing infrastructure: A method based on time series forecasting and multiple thresholds. In: 2011 IEEE Third International Workshop on Software Aging and Rejuvenation (WoSAR), pp 38–43. https://doi.org/10.1109/WoSAR.2011.18

  3. Ataie E, Entezari-Maleki R, Rashidi L, Trivedi KS, Ardagna D, Movaghar A (2017) Hierarchical stochastic models for performance, availability, and power consumption analysis of iaas clouds. IEEE Trans Cloud Comput

  4. Baccarelli E, Amendola D, Cordeschi N (2015) Minimum-energy bandwidth management for qos live migration of virtual machines. Comput Netw 93:1–22

    Article  Google Scholar 

  5. Beloglazov A, Buyya R (2010) Energy efficient allocation of virtual machines in cloud data centers. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), pp 577–578. IEEE

  6. CDW: Cdws cloud 401 report. Report, CDW (2015)

  7. Chang X, Wang T, Rodrguez RJ, Zhang Z (2018) Modeling and analysis of high availability techniques in a virtualized system. Comput J 61(2):180–198. https://doi.org/10.1093/comjnl/bxx049

    Article  Google Scholar 

  8. CISCO: Cisco global cloud networking survey summary and analysis of results worldwide results. Tech. rep., CISCO (2012)

  9. Clark C, Fraser K, Hand S, Hansen JG, Jul E, Limpach C, Pratt I, Warfield A (2005) Live migration of virtual machines. In: Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation-Volume 2, pp 273–286. USENIX Association

  10. Cotroneo D, Natella R, Pietrantuono R, Russo S (2014) A survey of software aging and rejuvenation studies. ACM J Emerg Technol Comput Syst (JETC) 10(1):8

    Google Scholar 

  11. Dantas J, Matos R, Araujo J, Maciel P (2015) Eucalyptus-based private clouds: availability modeling and comparison to the cost of a public cloud. Computing 97(11):1121–1140

    Article  MathSciNet  MATH  Google Scholar 

  12. Dell datacenter capacity planner tool (2017). URL http://www.dell.com/html/us/products/rack_advisor_new/index.html

  13. de Melo MDT, Araujo J, Umesh I, Maciel PRM (2017) Sware: an approach to support software aging and rejuvenation experiments. J Adv Theor Appl Inform 3(1):31–38

    Article  Google Scholar 

  14. Dohi T, Goseva-Popstojanova K, Trivedi KS (2000) Statistical non-parametric algorithms to estimate the optimal software rejuvenation schedule. In: 2000 Pacific Rim International Symposium on Dependable Computing, 2000. Proceedings. IEEE, pp 77–84

  15. Dohi T, Goševa-Popstojanova K, Trivedi K (2001) Estimating software rejuvenation schedules in high-assurance systems. Comput J 44(6):473–485

    Article  MATH  Google Scholar 

  16. Eia - u.s. energy information administration. electricity - state electricity profiles (2017). URL https://www.eia.gov/electricity/state/

  17. Garg S, Puliafito A, Telek M, Trivedi KS (1995) Analysis of software rejuvenation using markov regenerative stochastic petri net. In: Sixth International Symposium on Software Reliability Engineering, 1995. Proceedings., pp 180–187. https://doi.org/10.1109/ISSRE.1995.497656

  18. German R, Kelling C, Zimmermann A, Hommel G (1995) Timenet: a toolkit for evaluating non-markovian stochastic petri nets. Perform Eval 24(1):69–87

    Article  MATH  Google Scholar 

  19. Gong C, Liu J, Zhang Q, Chen H, Gong Z (2010) The characteristics of cloud computing. In: 2010 39th International Conference on Parallel Processing Workshops pp 275–279. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5599083

  20. Grottke M, Trivedi K (2005) A classification of software faults. J Reliab Eng Assoc Jpn 27(7):425–438

    Google Scholar 

  21. Grottke M, Trivedi KS (2007) Fighting bugs: remove, retry, replicate, and rejuvenate. Computer 40(2):107–109

    Article  Google Scholar 

  22. Huang Q, Gao F, Wang R, Qi Z (2011) Power consumption of virtual machine live migration in clouds. In: 2011 Third International Conference on Communications and Mobile Computing, pp 122–125. https://doi.org/10.1109/CMC.2011.62

  23. Huang Y, Kintala C, Kolettis N, Fulton ND (1995) Software rejuvenation: analysis, module and applications. In: Twenty-Fifth International Symposium on Fault-Tolerant Computing, 1995. FTCS-25. Digest of Papers. IEEE, pp 381–390

  24. Kourai K, Chiba S (2007) A fast rejuvenation technique for server consolidation with virtual machines. In: 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN’07). IEEE, pp 245–255

  25. Kourai K, Chiba S (2011) Fast software rejuvenation of virtual machine monitors. IEEE Trans Dependable Secur Comput 8(6):839–851

    Article  Google Scholar 

  26. Lagar-Cavilla HA, Whitney JA, Scannell AM, Patchin P, Rumble SM, de Lara E, Brudno M, Satyanarayanan M (2009) Snowflock: Rapid virtual machine cloning for cloud computing. In: Proceedings of the 4th ACM European Conference on Computer Systems, EuroSys ’09, pp 1–12. ACM, New York, NY, USA. https://doi.org/10.1145/1519065.1519067

  27. Liu B, Chang X, Han Z, Trivedi K, Rodríguez RJ (2018) Model-based sensitivity analysis of iaas cloud availability. Future Gener Comput Syst

  28. Liu H, Jin H, Liao X, Hu L, Yu C (2009) Live migration of virtual machine based on full system trace and replay. In: Proceedings of the 18th ACM International Symposium on High Performance Distributed Computing. ACM, pp 101–110

  29. Machida F, Kim DS, Trivedi KS (2010) Modeling and analysis of software rejuvenation in a server virtualized system. In: 2010 IEEE Second International Workshop on Software Aging and Rejuvenation (WoSAR). IEEE, pp 1–6

  30. Machida F, Kim DS, Trivedi KS (2013) Modeling and analysis of software rejuvenation in a server virtualized system with live vm migration. Perform Eval 70(3):212–230

    Article  Google Scholar 

  31. Malhotra M, Trivedi KS (1994) Power-hierarchy of dependability-model types. IEEE Trans Reliab 43(3):493–502

    Article  Google Scholar 

  32. Marsan MA, Balbo G, Conte G, Donatelli S, Franceschinis G (1994) Modelling with generalized stochastic Petri nets. Wiley, Hoboken

    MATH  Google Scholar 

  33. Matos R, Araujo J, Alves V, Maciel P (2012) Experimental evaluation of software aging effects in the eucalyptus elastic block storage. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp 1103–1108

  34. Melo M, Araujo J, Matos R, Menezes J, Maciel P (2013) Comparative analysis of migration-based rejuvenation schedules on cloud availability. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics . IEEE, pp 4110–4115

  35. Melo M, Maciel P, Araujo J, Matos R, Araújo C (2013) Availability study on cloud computing environments: Live migration as a rejuvenation mechanism. In: 2013 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, pp 1–6

  36. Nguyen TA, Min D, Choi E (2018) A comprehensive evaluation of availability and operational cost for a virtualized server system using stochastic reward nets. J Supercomput 74(1):222–276

    Article  Google Scholar 

  37. Okamura H, Dohi T (2013) Dynamic software rejuvenation policies in a transaction-based system under markovian arrival processes. Perform Eval 70(3):197–211

    Article  Google Scholar 

  38. Park K, Kim S (2002) Availability analysis and improvement of active/standby cluster systems using software rejuvenation. J Syst Softw 61(2):121–128

    Article  Google Scholar 

  39. Parnas DL (1994) Software aging. In: Proceedings of the 16th International Conference on Software Engineering. IEEE Computer Society Press, pp 279–287

  40. Royce WW (1970) Managing the development of large software systems. In: Proceedings of IEEE WESCON, vol 26. Los Angeles

  41. Strunk A, Dargie W (2013) Does live migration of virtual machines cost energy? In: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA). IEEE, pp 514–521

  42. Thein T, Chi SD, Park JS (2008) Availability modeling and analysis on virtualized clustering with rejuvenation. Int J Comput Sci Netw Secur 8(9):72–80

    Google Scholar 

  43. Thein T, Park JS (2009) Availability analysis of application servers using software rejuvenation and virtualization. J Comput Sci Technol 24(2):339–346

    Article  Google Scholar 

  44. Torquato M, Araujo J, Umesh I, Maciel P (2018) Sware: a methodology for software aging and rejuvenation experiments. J Inf Syst Eng Manag 3(2):15

    Google Scholar 

  45. Torquato M, Maciel P, Araujo J, Umesh I (2017) An approach to investigate aging symptoms and rejuvenation effectiveness on software systems. In: 2017 12th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, pp 1–6

  46. Trivedi KS, Bobbio A (2017) Reliability and availability engineering: modeling, analysis, and applications. Cambridge University Press, Cambridge

    Book  Google Scholar 

  47. Trivedi KS, Vaidyanathan K, Goseva-Popstojanova K (2000) Modeling and analysis of software aging and rejuvenation. In: 33rd Annual on Simulation Symposium, 2000. (SS 2000) Proceedings. IEEE, pp 270–279

  48. Vaidyanathan K, Trivedi KS (1999) A measurement-based model for estimation of resource exhaustion in operational software systems. In: issre, p 84. IEEE

  49. Vaidyanathan K, Trivedi KS (2001) Extended classification of software faults based on aging. In: Fast Abstract, International Symposium on Software Reliability Eng., Hong Kong

  50. Vaidyanathan K, Trivedi KS (2005) A comprehensive model for software rejuvenation. IEEE Trans Dependable Secur Comput 2(2):124–137

    Article  Google Scholar 

  51. Zhang T, Xie M, Horigome M (2006) Availability and reliability of k-out-of-(m+n): g warm standby systems. Reliab Eng Syst Saf 91(4):381–387

    Article  Google Scholar 

  52. Zimmermann A (2017) Modelling and performance evaluation with timenet 4.4. In: International Conference on Quantitative Evaluation of Systems. Springer, pp 300–303

Download references

Author information

Authors and Affiliations

  1. Federal Institute of Alagoas, Campus Arapiraca, Arapiraca, AL, Brazil

    Matheus Torquato

  2. Bharathiar University, Coimbatore, Tamil Nadu, India

    I M Umesh

  3. Center of Informatics - Federal University of Pernambuco, Recife, PE, Brazil

    Paulo Maciel

Authors
  1. Matheus Torquato
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. I M Umesh
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Paulo Maciel
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Matheus Torquato.

Appendix A. A brief description of TimeNET tool

Appendix A. A brief description of TimeNET tool

This description is based on the paper [52].

TimeNET is a tool for modeling and evaluation of several variants of SPNs. It allows the user to calculate reward measures from the models. It also supports evaluation of models with exponential, deterministic and non-exponential transitions. The tool provides numerical analysis and simulation methods to compute transient and steady-state solutions for the models. TimeNET also supports Colored Petri Nets.

The software is built using a Java graphical interface, shell scripts and C++ algorithms. The tool is available on 32- and 64-bit versions for Windows and Linux.

TimeNET tool is developed and maintained by the Systems and Software Engineering Group, TU Ilmenau, Germany. It is available free of charge for non-commercial use from http://timenet.tu-ilmenau.de/.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Torquato, M., Umesh, I.M. & Maciel, P. Models for availability and power consumption evaluation of a private cloud with VMM rejuvenation enabled by VM Live Migration. J Supercomput 74, 4817–4841 (2018). https://doi.org/10.1007/s11227-018-2485-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2485-4

Keywords