Skip to main content

Advertisement

SpringerLink
Log in

Energy efficiency for edge multimedia elastic applications

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

With the rise of edge computing paradigms, multimedia applications will have to tackle unprecedented management issues, pursuing an optimal balance between performance, Quality of Service (QoS), and power consumption. In this paper, we investigate a novel paradigm to deploy multimedia elastic applications at the edge in a very energy-efficient manner. Our approach is based on pre-provisioning virtual resources that remain “frozen” until the application scales out. Frozen resources are treated in a special way by the infrastructure, leveraging aggressive power-saving mechanisms that keep negligible their impact on energy consumption and performance. We report extensive measurements on QoS and power consumption that we carried out in a real testbed, which is the first working implementation of the proposed paradigm. Our work shows how resource utilization and performance can be increased by leveraging SDN technologies and conscious setting of cloud parameters. We investigate the trade-off between performance and power consumption (i.e., energy efficiency), in relation to different consolidation strategies. Finally, we measure power consumption and estimate energy saving for an elastic video transcoding application deployed at the network edge.

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

Similar content being viewed by others

Notes

  1. Amazon Elastic Transcoder: https://aws.amazon.com/elastictranscoder/.

  2. The Advanced Configuration and Power Interface (ACPI) [32] is an industry-led specification that brings power management under the control of the operating system, instead of delegating to platform-specific firmware. The S3 state consists in shutting down most hardware but the system RAM, which remains powered to quickly resume the whole system to full operation.

  3. Metadata can be inserted when the VM is created, but can also be updated later. See: https://wiki.openstack.org/wiki/NovaImageCreationAPI.

  4. Configuration of QoS parameters (priority queues, traffic shapers, scheduling disciplines) in the physical switches is not implemented yet, but it is already on the development roadmap.

  5. https://iperf.fr/.

  6. In this case, we deployed an additional server instead of the network node.

  7. OpenStack documentation – Configure live migrations. URL: https://docs.openstack.org/nova/pike/admin/configuring-migrations.html.

  8. Stress-ng is a tool that stresses a computer system in various selectable ways. Stress-ng has a wide range of CPU-specific stress tests that exercise floating point, integer, bit manipulation and control flow. URL: http://kernel.ubuntu.com/~cking/stress-ng/.

References

  1. Baker T, Asim M, Tawfik H, Aldawsari B, Buyya R (2017) An energy-aware service composition algorithm for multiple cloud-based IoT applications. J Netw Comput Appl 89(1):96–108

    Article  Google Scholar 

  2. Barroso LA, Hölzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37

    Article  Google Scholar 

  3. Beck MT, Feld S, Fichtner A, Popien CL, Schimper T (2015) ME- VoLTE: network functions for energy-efficient video transcoding at the mobile edge. In: Proc. of 18th Intl. conf on intelligence in next generation networks (ICIN). Paris

  4. Beloglazov A, Buyya R (2015) OpenStack Neat: a framework for dynamic and energy-efficient consolidation of virtual machines in OpenStack clouds. Concurr Comput Pract Exper 27(5):1310–1333

    Article  Google Scholar 

  5. Bierman A, Bjorklund M, Watsen K (2017) RESTCONF protocol. RFC 8040. https://tools.ietf.org/html/rfc8040

  6. Bolla R, Repetto M (2013) A comprehensive tutorial for mobility management in data networks. Commun Surv Tutor IEEE PP(99):1–22

    Google Scholar 

  7. Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. In: Proceedings of the 2010 international conference on parallel and distributed processing techniques and applications (PDPTA 2010). Las Vegas

  8. Carrega A, Repetto M (2017) Energy-aware consolidation scheme for data center cloud applications. In: First International workshop on softwarized infrastructures for 5G and fog computing (Soft5 2017). Genoa

  9. Carrega A, Portomauro G, Repetto M, Robino G (2018) OpenStack extensions for QoS and energy efficiency in edge computing. In: 3rd IEEE International conference on fog and edge mobile computing (FMEC 2018). Barcelona, http://www.reti.dist.unige.it/matteo/papers/qosvsee.pdf, to appear

  10. Chen M (2012) AMVSC: a framework of adaptive mobile video streaming in the cloud. In: Proc. of IEEE global communications conference (GLOBECOM). Anaheim, pp 2042–2047

  11. Cheng R, Wu W, Lou Y, Chen Y (2014) A cloud-based transcoding framework for real-time mobile video conferencing system. In: Proc. of 2nd IEEE intl. conf.on mobile cloud computing, services and engineering (MobileCloud). London

  12. Cima V, Grazioli B, Murphy S, Bohnert T (2015) Adding energy efficiency to Openstack. In: Sustainable internet and ICT for sustainability (SustainIT). Madrid, pp 1–8

  13. Dutta S, Taleb T, Frangoudis PA, Ksentini A (2016) On-the-fly QoE-aware transcoding in the mobile edge. In: Proc of IEEE global communications conference (GLOBECOM). Washington, DC

  14. ETSI (2016) Mobile edge computing (MEC); framework and reference architecture. ETSI GS MEC 003. http://www.etsi.org/deliver/etsi_gs/MEC/001_099/003/01.01.01_60/gs_MEC003v010101p.pdf, v1.1.1

  15. Fajardo JO, Taboada I, Liberal F (2015) Improving content delivery efficiency through multi-layer mobile edge adaptation. IEEE Netw 29(6):40–46

    Article  Google Scholar 

  16. Heller B, Seetharaman S, Mahadevan P, Yiakoumis Y, Sharma P, Banerjee S, McKeown N (2010) Elastictree: saving energy in data center networks. In: Proc. of the 7th USENIX conf. on net. sys. des. and impl., USENIX Association, Berkeley, CA, USA, NSDI, pp 17–17

  17. Jokhio F, Deneke T, Lafond S, Lilius J (2012) Bit rate reduction video transcoding with distributed computing. In: Proc. of 20th Intl. conf. on parallel, distributed and network-based processing (PDP). Garching, pp 206–212

  18. Kim M, Han S, Cui Y, Lee H, Cho H, Hwang S (2014) CloudDMSS: robust hadoop-based multimedia streaming service architecture for a cloud computing environment. Clust Comput 17(3):605–628

    Article  Google Scholar 

  19. Lao F, Zhang X, Guo Z (2012) Parallelizing video transcoding using map-reduce-based cloud computing. In: Proc. of IEEE Intl symposium on circuits and systems (ISCAS). Seoul

  20. Li B, Li J, Huai J, Wo T, Li Q, Zhong L (2009) Enacloud: an energy-saving application live placement approach for cloud computing environments. In: IEEE International conference on cloud computing (CLOUD ’09). Bangalore, pp 17–24

  21. Rossigneux F, Gelas JP, Lefèvre L, de Asunção MD (2014) A generic and extensible framework for monitoring energy consumption of openstack clouds. In: 4th IEEE International conference on sustainable computing and communications (SustainCom). Sydney

  22. Schwarz H, Marpe D, Wiegand T (2007) Overview of the scalable video coding extension of the H.264/AVC standard. IEEE Trans Circ Syst Video Technol 17 (9):1103–1120

    Article  Google Scholar 

  23. Shirayanagi H, Yamada H, Kono K (2012) Honeyguide: a VM migration-aware network topology for saving energy consumption in data center networks. In: IEEE Symposium on computers and communications (ISCC). Cappadocia, pp 460–467

  24. Tran TX, Pandey P, Hajisami A, Pompili D (2016) Collaborative multi-bitrate video caching and processing in mobile-edge computing networks. arXiv:1612.01436

  25. Voorsluys W, Broberg J, Venugopal S, Buyya R (2009) Cost of virtual machine live migration in clouds: a performance evaluation. In: Proceedings of the 1st international conference on cloud computing (CloudCom ’09). LNCS: Lecture Notes In Computer Science, pp 254–265

  26. Wang F, Liu J, Chen M (2012) CALMS: cloud-assisted live media streaming for globalized demands with time/region diversities. In: Proc. of IEEE conference on computer communications (INFOCOM). Orlando, pp 199-207

  27. Wu Y, Wu C, Li B, Lau FCM, 2013 vSkyConf: cloud-assisted multiparty mobile video conferencing. In: Proc. of the 2nd ACM SIGCOMM workshop on mobile cloud computing. Hong Kong, pp 33–38

  28. Zhang W, Wen Y, Chen HH (2014) Toward transcoding as a service: energy-efficient offloading policy for green mobile cloud. IEEE Netw 28(6):67–73

    Article  Google Scholar 

  29. Zhao Y, Jiang H, Zhou K, Huang Z, Huang P (2014) Meeting service level agreement cost-effectively for video-on-demand applications in the cloud. In: Proc. of IEEE conf. on computer communications (INFOCOM). Toronto, pp 298–306

  30. (2013) Topology and orchestration specification for cloud applications. OASIS Standard. http://docs.oasis-open.org/tosca/TOSCA/v1.0/os/TOSCA-v1.0-os.pdf, version 1.0

  31. (2014) Network functions virtualisation (nfv); management and orchestration. ETSI GS NFV-MAN 001. http://www.etsi.org/deliver/etsi_gs/NFV-MAN/001_099/001/01.01.01_60/gs_nfv-man001v010101p.pdf, v1.1.1

  32. (2017) ACPI Specification Version 6.2 (Errata A). Unified Extensible Firmware Interface Forum

Download references

Acknowledgments

This work was supported in part by the European Commission under the projects ARCADIA (contract no. 645372) and MATILDA (contract no. 761898).

Author information

Authors and Affiliations

  1. CNIT – S3ITILab, Via all’Opera Pia 13, 16145, Genoa, Italy

    Alessandro Carrega, Matteo Repetto & Giorgio Robino

  2. University of Genoa – TNT Lab, Via all’Opera Pia 13, 16145, Genoa, Italy

    Giancarlo Portomauro

Authors
  1. Alessandro Carrega
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giancarlo Portomauro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matteo Repetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giorgio Robino
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alessandro Carrega.

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

Carrega, A., Portomauro, G., Repetto, M. et al. Energy efficiency for edge multimedia elastic applications. Multimed Tools Appl 78, 24739–24764 (2019). https://doi.org/10.1007/s11042-018-7050-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-7050-x

Keywords

Search

Navigation