Advertisement

Photonic Network Communications

, Volume 29, Issue 3, pp 269–281 | Cite as

Optical wireless network convergence in support of energy-efficient mobile cloud services

  • Markos P. AnastasopoulosEmail author
  • Anna Tzanakaki
  • Bijan Rahimzadeh Rofoee
  • Shuping Peng
  • Yan Yan
  • Dimitra Simeonidou
  • Giada Landi
  • Giacomo Bernini
  • Nicola Ciulli
  • Jordi Ferrer Riera
  • Eduard Escalona
  • Kostas Katsalis
  • Thanasis Korakis
Article

Abstract

Mobile computation offloading has been identified as a key-enabling technology to overcome the inherent processing power and storage constraints of mobile end devices. To satisfy the low-latency requirements of content-rich mobile applications, existing mobile cloud computing solutions allow mobile devices to access the required resources by accessing a nearby resource-rich cloudlet, suffering increased capital and operational expenditures. To address this issue, in this paper, we propose an infrastructure and architectural approach based on the orchestrated planning and operation of optical data center networks and wireless access networks. To this end, a novel formulation based on a multi-objective nonlinear programming model is presented that considers energy-efficient virtual infrastructure planning over the converged wireless, optical network interconnecting DCs with mobile devices, taking a holistic view of the infrastructure. Our modelling results identify trends and trade-offs relating to end-to-end service delay, mobility, resource requirements and energy consumption levels of the infrastructure across the various technology domains.

Keywords

Mobile cloud computing Energy efficiency Queuing theory Virtual infrastructure planning Converged infrastructures 

Notes

Acknowledgments

This work was carried out with the support of the CONTENT (FP7-ICT- 318514) project funded by the EC through the 7th ICT Framework Program.

References

  1. 1.
    Rappa, M.: The utility business model and the future of computing systems. IBM Syst. J. 43(1), 32–42 (2004)CrossRefGoogle Scholar
  2. 2.
    Fiorani, M., Aleksic, S., Monti, P., Chen, J., Casoni, M., Wosinska, L.: Energy efficiency of an integrated intra-data-center and core network with edge caching. J. Opt. Commun. Netw. 6, 421–432 (2014)Google Scholar
  3. 3.
    Wiatr, P., Chen, J., Monti, P., Wosinska, L.: Energy efficiency and reliability tradeoff in optical core networks. In: Proceedings of OFC 2014, paper Th4E.4 (2014)Google Scholar
  4. 4.
    Mandal, U., Habib, M., Shuqiang, Z., Mukherjee, B., Tornatore, M.: Greening the cloud using renewable-energy-aware service migration. IEEE Netw. 27(6), 36–43 (2013)CrossRefGoogle Scholar
  5. 5.
    Dinh, H., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 13(18), 1587–1611 (2013)CrossRefGoogle Scholar
  6. 6.
    Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2012–2017. White Paper (2013)Google Scholar
  7. 7.
    Mun, K.: Mobile cloud computing challenges. TechZine Magazine. http://www2.alcatel-lucent.com/techzine/mobile-cloud-computing-challenges/
  8. 8.
    Satyanarayanan, M., et al.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 14–23 (2009)CrossRefGoogle Scholar
  9. 9.
  10. 10.
  11. 11.
    Weiwen, Z., Yonggang, W., Wu, D.O.: Energy-efficient scheduling policy for collaborative execution in mobile cloud computing. In: Proceedings of IEEE INFOCOM 2013, pp. 190–194 (2013)Google Scholar
  12. 12.
    Rahman, M., Gao, J., Wei-Te, T.: Energy saving in mobile cloud computing. In: Proceedings of IEEE IC2E 2013, pp. 285–291 (2013)Google Scholar
  13. 13.
    Hyytiä, E., Spyropoulos, T., Ott, J.: Optimizing offloading strategies in mobile cloud computing. https://www.netlab.tkk.fi/u/esa/pub/files/hyytia-subm2-2013.pdf (2013)
  14. 14.
    Zhang, Y., Niyato D., Wang, P., Tham, C.K.: Dynamic offloading algorithm in intermittently connected mobile cloudlet systems. In: Proceedings of IEEE ICC, Sydney (2014)Google Scholar
  15. 15.
    Barbarossa, S., Sardellitti, S., Di Lorenzo, P.: Computation offloading for mobile cloud computing based on wide cross-layer optimization. In: Proceedings of FutureNetworkSummit (2013)Google Scholar
  16. 16.
    Peng, S., Fangming, L., Hai, J., Min, C., Feng, W., Yupeng, Q.: eTime: energy-efficient transmission between cloud and mobile devices. In: Proceedings of IEEE INFOCOM 2013, pp. 195–199 (2013)Google Scholar
  17. 17.
    Kaewpuang, R., Niyato, D., Ping, W., Hossain, E.: A framework for cooperative resource management in mobile cloud computing. IEEE J. Sel. Areas Commun. 31(12), 2685–2700 (2013)CrossRefGoogle Scholar
  18. 18.
    Chen, X.: Decentralized computation offloading game for mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2014.2316834 (2014)
  19. 19.
    Kumar, K., Liu, J., Lu, Yung-Hsiang, Bhargava, B.: A survey of computation offloading for mobile systems. Mob. Netw. Appl. 18(1), 129–140 (2013)CrossRefGoogle Scholar
  20. 20.
    Sanaei, Z., Abolfazli, S., Gani, A., Buyya, R.: Heterogeneity in mobile cloud computing: taxonomy and open challenges. IEEE Commun. Surv. Tutor. 16(1), 369–392 (2014). First QuarterCrossRefGoogle Scholar
  21. 21.
  22. 22.
    Tzanakaki, A., et al.: Virtualization of heterogeneous wireless-optical network and IT infrastructures in support of cloud and mobile cloud services. IEEE Commun. Mag. 51(8), 155–161 (2013)CrossRefGoogle Scholar
  23. 23.
    Hou, W., Yu, C., Zong, Y.: A novel dynamic virtual infrastructure planning for converged optical network and data centers under power outage and evolving recovery. Opt. Switch. Netw. 14(3), 209–216 (2014)CrossRefGoogle Scholar
  24. 24.
    Hou, W., Guo, L., Liu, Y., Song, Q., Wei, X.: Virtual network planning for converged optical and data centers: ideas and challenges. IEEE Netw. 27(6), 52–58 (2013)CrossRefGoogle Scholar
  25. 25.
    Wang, A., Iyer, M., Dutta, R., Rouskas, G., Baldine, I.: Network virtualization: technologies, perspectives, and frontiers. J. Lightwave Technol. 31(4), 523–537 (2013)CrossRefGoogle Scholar
  26. 26.
    MAINS project website. http://www.ist-mains.eu/
  27. 27.
    Giatsios, D., Apostolaras, A., Korakis, T., Tassiulas, L.: Methodology and tools for measurements on wireless testbeds: the nitos approach. Measurement Methodology and Tools, LNCS, vol. 7586, pp. 61–80. Springer, Berlin (2013)CrossRefGoogle Scholar
  28. 28.
    Schupke, D.: Multilayer and multidomain resilience in optical networks. Proc. IEEE 100(5), 1140–1148 (2012)CrossRefGoogle Scholar
  29. 29.
    Chang, J., Lim, K.T., Byrne, J., Ramirez, L., Ranganathan, P.: Workload diversity and dynamics in big data analytics: implications to system designers. In: Proceedings of ASBD ’12 (2012)Google Scholar
  30. 30.
    Baskett, F., Chandy, K.M., Muntz, R.R., Palacios, F.G.: Open, closed, and mixed networks of queues with different classes of customers. J. ACM 22(2), 248–260 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  31. 31.
    Katrinis, K.M., Tzanakaki, A.: On the dimensioning of WDM optical networks with impairment-aware regeneration. IEEE/ACM Trans. Netw. 19(3), 735–746 (2011)CrossRefGoogle Scholar
  32. 32.
    Davis, Z.: Power consumption and cooling in the data center: a survey (online). http://www.greenbiz.com/
  33. 33.
    Kumar, K., Lu, Y.-H.: Cloud computing for mobile users: can offloading computation save energy? Computer 43(4), 51–56 (2010)CrossRefGoogle Scholar
  34. 34.
    Carroll, A., Heiser, G.: An analysis of power consumption in a smartphone. In: Proceedings of the 2010 USENIX, Berkeley, CA, USA, p. 21 (2010)Google Scholar
  35. 35.
    Ardito, L., et al.: Profiling power consumption on mobile devices. In: Proceedings of the \(3^{{\rm rd}}\) International Conference on Smart Grids, Green Communications and IT Energy-Aware Technologies, pp. 101–106 (2013)Google Scholar
  36. 36.
    Eichfelder, G.: Adaptive Scalarization Methods in Multiobjective Optimization. Springer, Berlin (2008)CrossRefzbMATHGoogle Scholar
  37. 37.
    Auer, G., Giannini, V.: Cellular energy efficiency evaluation framework. In: Proceedings of IEEE VTC (2011)Google Scholar
  38. 38.
  39. 39.
    3GPP TS 23.203. Technical Specification Group Services and System AspectsGoogle Scholar
  40. 40.
    Chang, J., Lim, K.T., Byrne, J., Ramirez, L., Ranganathan, P.: Workload diversity and dynamics in big data analytics: implications to system designers. In: Proceedings of ASBD ’12. New York, NY, USA, pp. 21–26 (2012)Google Scholar
  41. 41.
    Fang, Y., Chlamtac, I.: Analytical generalized results for handoff probability in wireless networks. IEEE Trans. Commun. 50(3), 369–399 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Markos P. Anastasopoulos
    • 1
    Email author
  • Anna Tzanakaki
    • 1
  • Bijan Rahimzadeh Rofoee
    • 1
  • Shuping Peng
    • 1
  • Yan Yan
    • 1
  • Dimitra Simeonidou
    • 1
  • Giada Landi
    • 2
  • Giacomo Bernini
    • 2
  • Nicola Ciulli
    • 2
  • Jordi Ferrer Riera
    • 3
  • Eduard Escalona
    • 3
  • Kostas Katsalis
    • 4
  • Thanasis Korakis
    • 4
  1. 1.University of BristolBristolUK
  2. 2.NextworksPisaItaly
  3. 3.i2CATBarcelonaSpain
  4. 4.University of ThessalyVolosGreece

Personalised recommendations