Skip to main content

Advertisement

SpringerLink
Log in

Virtual network mapping considering energy consumption and availability

  • Published:
Computing Aims and scope Submit manuscript

Abstract

Network virtualization is widely considered as a mainstay for overcoming the Internet’s ossification problem, and virtual network embedding (VNE) is a critical issue. Over recent years, growing energy costs and increased ecological awareness have stimulated the interest in reducing energy consumption by Internet service providers (ISP). Dependability is also an important requirement, as it involves metrics such as reliability and availability, which directly impact quality of service (QoS). Prior works on virtual network embedding have focused mainly on maximizing revenue for Internet service providers (ISPs), and they did not consider energy consumption and dependability metrics jointly in the mapping. This paper presents an energy-efficient mapping of dependable virtual networks. The approach considers a problem formulation that concomitantly takes into account energy consumption and availability constraints for virtual network embedding problem, and an algorithm based on Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic is adopted. The algorithm utilizes a sensitivity analysis based on availability importance to achieve the QoS required by each virtual network, and models based on reliability block diagrams (RBD) and stochastic Petri nets (SPN) are utilized to estimate availability. Results demonstrate the feasibility of the proposed approach, and they show the trade-off between availability, energy consumption, cost and revenue.

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
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. An inefficient truth, Global Action Plan, London, Dec. 2007 [online]. Available: http://www.globalactionplan.org.uk/. Accessed 03 Mar 2016

  2. Balbo G (2001) Introduction to stochastic Petri nets. In: Lectures on formal methods and performance analysis

  3. Botero JF et al (2012) Energy efficient virtual network embedding. IEEE Commun Lett 16:756–759

    Article  Google Scholar 

  4. Chabarek J et al (2008) Power awareness in network design and routing. In: Chabarek J, Sommers J, Barford P (eds) Proceedings of the IEEE INFOCOM

  5. Chiaraviglio L et al (2012) Minimizing ISP network energy cost: formulation and solutions. IEEE/ACM Trans Netw 20(2):163,476

    Article  Google Scholar 

  6. Chowdhury N et al (2009) Virtual network embedding with coordinated node and link mapping. In: Proceedings of the IEEE INFOCOM

  7. Chowdhury SR et al (2016) Dedicated protection for survivable virtual network embedding. IEEE Trans Netw Serv Manag 13(4):913–926

    Article  Google Scholar 

  8. Fernandes Stenio et al (2012) Dependability Assessment of Virtualized Networks. In: Proceedings of the IEEE ICC 2012 next-generation networking symposium (ICC12 NGN) in conjunction with IEEE international conference on communications ICC 2012, Ottawa

  9. Gong S et al (2016) Energy-efficient virtual network embedding for heterogeneous networks. In: 2016 first IEEE international conference on computer communication and the internet (ICCCI), Wuhan, pp 85–90

  10. Iannacconeand G et al (2002) Analysis of link failures in an IP backbone. In: Proceedings on 2002 ACM SIGCOMM workshop on internet measurment

  11. Jarray A, Karmouch A (2012) Column generation approach for one-shot virtual network embedding. In: Proceedings of the GlobeCom

  12. Khan MMA et al (2016) Multi-path link embedding for survivability in virtual networks. IEEE Trans Netw Serv Manag 13(2):253–266

    Article  Google Scholar 

  13. Koslovski G et al (2010) Reliability support in virtual infrastructures. In: Proceedings of the IEEE 2nd international conference on cloud computing technology and science, pp 49–58

  14. Kuo W, Zuo M (2003) Optimal reliability modeling: principles and applications. Wiley, Hoboken

    Google Scholar 

  15. Lira V et al (2014) Dependable virtual network mapping. Springer Computing. ISSN: 0010-485X (print) 1436–5057 (online)

  16. Lira V et al (2015) An automated approach to dependability evaluation of virtual networks. Comput Netw 88:89–102 (ISSN 1389-1286)

    Article  Google Scholar 

  17. Lischka J, Karl H (2009) A virtual network mapping algorithm based on subgraph isomorphism detection. In: Proceedings of ACM SIGCOMM VISA

  18. Lu J, Turner J (2006) Efficient mapping of virtual networks onto a shared substrate. Technical Report WUCSE-2006-35, Washington University in St Louis, pp 1–11

  19. Maciel PRM et al (2010) Dependability modeling. In: Performance and dependability in service computing: concepts, techniques and research directions. Edward Hershey, IGI Global, PA

  20. Melo M et al (2013) Optimal virtual network embedding: node-link formulation. IEEE Trans Netw Serv Manag 10(4):356–368

    Article  Google Scholar 

  21. Onguetou DP, Grover WD (2008) A new insight and approach to node failure protection with ordinary p-cycles. In: Proceedings of the IEEE international conference on communication (ICC 08)

  22. Pagesand A et al (2012) Strategies for virtual optical network allocation. IEEE Comm Lett 16(2):268–271

    Article  Google Scholar 

  23. Pickavet M et al (2008) Worldwide energy needs for ICT: the rise of power-aware networking. In: Proceedings of the IEEE ANTS, Bombay, India, p 13

  24. Rahman MR et al (2010) Survivable virtual network embedding. Inl Proceedings of the 9th IFIP NETWORKING conference, Chennai

  25. Rahman M, Boutaba R (2013) SVNE: survivable virtual network embedding algorithms for network virtualization. IEEE Trans Netw Serv Manag 10(2):105–118

    Article  Google Scholar 

  26. Resende MGC et al (2001) Greedy randomized adaptive search procedures. In: Glover F, Kochenberger G (eds) State-of-the-art handbook in metaheuristics. Kluwer Academic Publisher, Dordrecht

    Google Scholar 

  27. Resende MGC et al (2010) GRASP and path relinking for the maxmin diversity problem. Comput Oper Res 37:498–508

    Article  MathSciNet  MATH  Google Scholar 

  28. Schaffrath G et al (2009) Network virtualization architecture: proposal and initial prototype. In: Proceedings of the 1st ACM workshop on virtualized infrastructure systems and architectures (VISA09)

  29. Schrijver A (1986) Theory of linear and integer programming. Wiley, NewYork

    MATH  Google Scholar 

  30. Silva B et al (2013) ASTRO: an integrated environment for dependability and sustainability evaluation. Sustain Comput Inform Syst 2:1–31

    Google Scholar 

  31. Sivaraman V et al (2011) Profiling perpacket and per-byte energy consumption in the netfpga gigabit router. In Proceedings of the IEEE INFOCOM WKSHPS

  32. Su S et al (2014) Energy-aware virtual network embedding. IEEE ACM Trans Netw 22(5):1607–1620

    Article  Google Scholar 

  33. Szeto W et al (2003) A multi-commodity flow based approach to virtual network resource allocation. In: Proceedings of the IEEE global telecommunications conference (GLOBECOM03), pp 3004–3008

  34. Texas-Instruments (2015) Evm430-f6736-msp430f6736 evm for metering. http://www.ti.com/tool/EVM430-F6736. Accessed 22 Mar 2016

  35. Trivedi K (2002) Probability and statistics with reliability, queueing, and computer science applications, 2nd edn. Wiley, Hoboken

    Google Scholar 

  36. USFERC, Washington, DC, USA, United States Federal Energy Regulatory Commission [online]. Available: http://www.ferc.gov. Accessed 03 Mar 2016

  37. Walllerstein S et al (1980) Some statistical methods useful in circulation research. Circ Res 47:19

    Google Scholar 

  38. Wang B et al (2012) Reducing power consumption in embedding virtual infrastructures. In: Proceeding of the IEEE globecom workshops, Dec 37, pp 714–718

  39. Yu Y et al (2011) RMap: an algorithm of virtual network resilience mapping. In: Proceedings of the seventh international conference on wireless communication, networking and mobile computing (WiCOM), pp 1–4

  40. Yu M et al (2008) Rethinking virtual network embedding: substrate support for path splitting and migration. ACM SIGCOMM Comput Commun Rev 38(2):1729

    Article  Google Scholar 

  41. Yu H et al (2012) A cost efficient design of virtual infrastructures with joint node and link mapping. J Netw Syst Manag 20:97–115

    Article  Google Scholar 

  42. Zegura E et al (1996) How to model an Internetwork. In: Proceedings of IEEE INFOCOM, pp 594–602

  43. Zhu Y, Ammar M (2006) Algorithms for assigning substrate network resources to virtual network components. In: Proceedings of IEEE INFOCOM

Download references

Author information

Authors and Affiliations

  1. Center of Informatics (CIn), Universidade Federal de Pernambuco (UFPE), Recife, Brazil

    Victor Lira & Eduardo Tavares

  2. Instituto Federal de Pernambuco (IFPE), Recife, Brazil

    Victor Lira & Meuse Oliveira Jr.

  3. Universidade Federal Rural de Pernambuco (UFRPE), Recife, Brazil

    Erica Sousa & Bruno Nogueira

Authors
  1. Victor Lira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eduardo Tavares
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meuse Oliveira Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erica Sousa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bruno Nogueira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Victor Lira.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lira, V., Tavares, E., Oliveira, M. et al. Virtual network mapping considering energy consumption and availability. Computing 101, 937–967 (2019). https://doi.org/10.1007/s00607-018-0620-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-018-0620-y

Keywords

Mathematics Subject Classification

Search

Navigation