Skip to main content

Advertisement

SpringerLink
Log in

A model-based strategy for quantifying the impact of availability on the energy flow of data centers

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

Abstract

The demand for higher computing power increases and, as a result, also leads to an increased demand for services hosted in cloud computing environments. It is known, for example, that in 2018 more than 4 billion people made daily access to these services through the Internet, corresponding to more than half of the world’s population. To support such services, these clouds are made available by large data centers. These systems are responsible for the increasing consumption of electricity, given the increasing number of accesses, increasing the demand for greater communication capacity, processing and high availability. Since electricity is not always obtained from renewable resources, the relentless pursuit of cloud services can have a significant environmental impact. In this context, this paper proposes an integrated and dynamic strategy that demonstrates the impact of the availability of data center architecture equipment on energy consumption. For this, we used the technique of modeling colored Petri nets (CPN), responsible for quantifying the cost, environmental impact and availability of the electricity infrastructure of the data centers under analysis. Such proposed models are supported by the developed tool, where data center designers do not need to know CPN to compute the metrics of interest. A case study was proposed to show the applicability of the proposed strategy. Significant results were obtained, showing an increase in system availability of 100%, with equivalents operating cost and environmental impact.

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

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Andrade E, Nogueira B, Matos R, Callou G, Maciel, P (2017) Availability modeling and analysis of a disaster-recovery-as-a-service solution. Computing pp 1–26

  2. Andrae AS, Edler T (2015) On global electricity usage of communication technology: trends to 2030. Challenges 6(1):117–157

    Article  Google Scholar 

  3. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I et al (2010) A view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  4. Avelar V (2003) Comparing availability of various rack power redundancy configurations. APC White Paper 48:1–22

    Google Scholar 

  5. Avizienis A, Laprie JC, Randell B et al (2001) Fundamental concepts of dependability. University of Newcastle upon Tyne, Computing Science

  6. Buyya R, Vecchiola C, Selvi ST (2013) Mastering cloud computing: foundations and applications programming, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco

    Google Scholar 

  7. Callou G, Maciel P, Tavares E, Andrade E, Nogueira B, Araujo C, Cunha P (2011) Energy consumption and execution time estimation of embedded system applications. Microprocess Microsyst 35(4):426–440

    Article  Google Scholar 

  8. CALLOU GRdA (2013) Assessment to support the planning of sustainable data centers with high availability. Universidade Federal de Pernambuco

  9. Chaczko Z, Mahadevan V, Aslanzadeh S, Mcdermid C (2011) Availability and load balancing in cloud computing. In: International Conference on Computer and Software Modeling, Singapore, vol 14

  10. Dincer I (2000) Thermodynamics, exergy and environmental impact. Energy Sources 22(8):723–732

    Article  Google Scholar 

  11. Ferreira J, Callou G, Tutsch D, Maciel P (2018) Pldadan algorihm to reduce data center energy consumption. Energies 11(10):2821

    Article  Google Scholar 

  12. Headquarters A (2007) Cisco data center infrastructure 2.5 design guide. Cisco Validated Design I. Cisco Systems, Inc

  13. Hewlett-Packard: Hp power advisor tool. http://h18004.www1.hp.com/products/solutions/power (2013)

  14. Huber P, Jensen K, Shapiro RM (1989) Hierarchies in coloured petri nets. In: International Conference on Application and Theory of Petri Nets, pp 313–341. Springer

  15. IEEE Gold Book 473, Design of Reliable Industrial and Commercial Power Systems. (2010)

  16. Janoušek V (1998) Modelling objects by petri nets. Ph.D. thesis, PhD. thesis, Brno University of Technology, Brno, Czech Republic

  17. Jensen K (1989) Coloured petri nets: A high level language for system design and analysis. In: International Conference on Application and Theory of Petri Nets, pp 342–416. Springer

  18. Jensen K (1997) A brief introduction to coloured petri nets. In: International Workshop on Tools and Algorithms for the Construction and Analysis of Systems, pp 203–208. Springer

  19. Jensen K, Kristensen LM (2009) Coloured Petri nets: modelling and validation of concurrent systems. Springer Science & Business Media

  20. Jensen K, Kristensen LM, Wells L (2007) Coloured petri nets and cpn tools for modelling and validation of concurrent systems. Int J Softw Tools Technol Transfer 9(3–4):213–254

    Article  Google Scholar 

  21. Kemp S (2020) Digital 2020: 3.8 billion people use social media. We are social

  22. Liu Y, Li X, Lin Y, Kang R, Xiao L (2017) A colored generalized stochastic petri net simulation model for service reliability evaluation of active-active cloud data center based on it infrastructure. In: 2017 2nd International Conference on System Reliability and Safety (ICSRS), pp 51–56. IEEE

  23. Low C, Chen Y, Wu M (2011) Understanding the determinants of cloud computing adoption. Ind Manage Data Syst 111(7):1006–1023

    Article  Google Scholar 

  24. Marsan MA (1988) Stochastic petri nets: an elementary introduction. In: European Workshop on Applications and Theory in Petri Nets, pp 1–29. Springer

  25. Melo C, Matos R, Dantas J, Maciel P (2017) Capacity-oriented availability model for resources estimation on private cloud infrastructure. In: IEEE 22nd Pacific Rim International Symposium on Dependable Computing (PRDC), 2017, pp 255–260. IEEE

  26. Merlin P, Farber D (1976) Recoverability of communication protocols-implications of a theoretical study. IEEE Trans Commun 24(9):1036–1043

    Article  MathSciNet  Google Scholar 

  27. Microsoft: Microsoft–creating a greener data center. http://www.microsoft.com/presspass/features/2009/apr09/04-02Greendatacenters.mspx (2009)

  28. Milner R (1997) The definition of standard ML: revised. MIT press

  29. Murata T (1989) Petri nets: Properties, analysis and applications. Proc IEEE 77(4):541–580

    Article  Google Scholar 

  30. Petri CA (1962) Communicating with automata. Germany: PhD thesis, Technical University Darmstadt

  31. Poess M, Nambiar RO (2008) Energy cost, the key challenge of today’s data centers: a power consumption analysis of tpc-c results. Proc. VLDB Endow 1(2):1229–1240. https://doi.org/10.14778/1454159.1454162

    Article  Google Scholar 

  32. Rivoire S, Ranganathan P, Kozyrakis C (2008) A comparison of high-level full-system power models. In: Proceedings of the 2008 Conference on Power Aware Computing and Systems, HotPower’08, pp 3–3. USENIX Association, Berkeley, CA, USA . http://dl.acm.org/citation.cfm?id=1855610.1855613

  33. Rocha É, Endo PT, Leoni G, Braga J, Lynn T (2017) Analyzing the impact of power infrastructure failures on cloud application availability. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 1746–1751. IEEE

  34. Sampaio AM, Barbosa JG (2018) A comparative cost analysis of fault-tolerance mechanisms for availability on the cloud. Sustainable Comput Inf Syst 19:315–323

    Google Scholar 

  35. Schmidt D (2009) Low exergy systems for high-performance buildings and communities. Energy Build 41(3):331–336

    Article  Google Scholar 

  36. Talebberrouane M, Khan F, Lounis Z (2016) Availability analysis of safety critical systems using advanced fault tree and stochastic petri net formalisms. J Loss Prev Process Ind 44:193–203

    Article  Google Scholar 

  37. Wiboonrat M (2008) An empirical study on data center system failure diagnosis. In: The Third International Conference on Internet Monitoring and Protection, 2008. ICIMP’08, pp 103–108. IEEE

Download references

Acknowledgements

The authors would like to thank CNPq and FACEPE for financing and supporting the development of this work.

Author information

Authors and Affiliations

  1. Department of Computing Federal Rural University of Pernambuco, Recife, Brazil

    Thiago Valentim & Gustavo Callou

Authors
  1. Thiago Valentim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gustavo Callou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thiago Valentim.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Valentim, T., Callou, G. A model-based strategy for quantifying the impact of availability on the energy flow of data centers. J Supercomput 77, 2566–2589 (2021). https://doi.org/10.1007/s11227-020-03353-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03353-4

Keywords

Search

Navigation