Skip to main content

Advertisement

SpringerLink
Log in

Placing it right!: optimizing energy, processing, and transport in Edge-Fog clouds

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

Abstract

In recent years, applications such as Internet-of-Things has proliferated the Internet to a great extent. Such applications derive data from a significant number of smart sensors sensing information from the environment. Due to an extensive data footprint, the demand for cloud services to process this data has also increased. However, traditional centralized cloud model requires offloading data from these sensors over a network which induces significant network delay on these applications. Several architectural abstractions of cloud, such as Fog and Edge, have been proposed to localize some of the processing near the sensors and away from the central cloud servers. In this paper, we propose Edge-Fog cloud which distributes task processing on the participating cloud resources in the network. We develop the Least Processing Cost First (LPCF) method for assigning the processing tasks to nodes which provide the optimal processing time and near-optimal networking costs. We further provide an energy-efficient variant of LPCF, i.e., eLPCF algorithm, which optimizes energy usage while calculating task deployment in Edge-Fog cloud. We evaluate both LPCF and eLPCF in a variety of scenarios and demonstrate its effectiveness in finding the processing task assignments.

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

Similar content being viewed by others

Notes

  1. This paper is an extended version of a paper with the same title published at International Conference on Cloudification of Internet of Things in 2016 [14]. The work proposed a unique solution to assignment problem which considered network and processing costs of jobs on the cloud. However, the proposed solution was unable to incorporate any other cost models which are highly relevant and motivate real-world deployments of edge clouds. The novel contributions of the current manuscript focus on energy dissipation which is real-world highly significant and a pressing issue, especially in battery-operated resources. This paper leverages the previously proposed solution and presents an extended algorithm which also incorporates energy dissipation costs of cloud resources while computing an assignment. We further implement, evaluate, and compare our proposed solution to our previous and related work. We also extend our Edge deployment discussion and analyze the effects of resource types on overall job deployment.

  2. Several incentive/credit mechanisms similar to crypto-currency mining mechanisms can be employed for devices to volunteer as Edge resource [15]. However, discussion of such mechanisms is currently out-of-scope of this paper.

  3. Throughout the paper, we use the terms devices and resources interchangeably.

  4. It must be noted that as a resource may be involved in more than one task deployments simultaneously, the processing ability of a resource refers to its currently available processing power prior to next task deployment

  5. In this paper, we only consider a non-weighted job dependencies and leave the weighted job graph mapping for future work.

References

  1. Broadband by the numbers, ‘https://www.ncta.com/broadband-by-the-numbers’. Accessed: 22 Apr 2015

  2. CISCO (2015) Cisco fog computing solutions: unleash the power of the internet of things (whitepaper). [Online]

  3. Yannuzzi M et al (2014) Key ingredients in an iot recipe: fog computing, cloud computing, and more fog computing. In: IEEE CAMAD

  4. Hong K et al (2013) Mobile fog: a programming model for large-scale applications on the internet of things. In: ACM SIGCOMM workshop on mobile cloud computing

  5. Garcia Lopez P et al (2015) Edge-centric computing: vision and challenges. SIGCOMM Comput Commun Rev 45(5):37–42

    Article  Google Scholar 

  6. Anderson DP et al (2002) Seti@home: an experiment in public-resource computing. Commun ACM 45 (11):56–61

    Article  Google Scholar 

  7. Beberg AL et al (2009) Folding@home: lessons from eight years of volunteer distributed computing. In: IEEE IPDPS

  8. Chandra A, Weissman J, Heintz B (2013) Decentralized edge clouds. IEEE Internet Comput 17(5):70–73

    Article  Google Scholar 

  9. Islam S, Grégoire J-C (2012) Giving users an edge: a flexible cloud model and its application for multimedia. Futur Gener Comput Syst 28(6):823–832

    Article  Google Scholar 

  10. Hu YC, Patel M, Sabella D, Sprecher N, Young V (2015) Mobile edge computing—a key technology towards 5G. ETSI white paper

  11. Verbelen T et al (2012) Cloudlets: bringing the cloud to the mobile user. In: ACM workshop on mobile cloud computing and services

  12. Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for VM-based cloudlets in mobile computing. In: IEEE pervasive computing

  13. Cordero IC, Orgerie A-C, Morin C (2015) GRaNADA: a network-aware and energy-efficient PaaS cloud architecture. In: IEEE international conference on green computing and communications (GreenCom)

  14. Mohan N, Kangasharju J (2016) Edge-fog cloud: a distributed cloud for internet of things computations. In: Proceedings of CIoT. IEEE

  15. BitCoin Mining Wiki, https://en.bitcoin.it/wiki/Mining

  16. Roman R, Lopez J, Mambo M (2018) Mobile edge computing, Fog et al.: a survey and analysis of security threats and challenges. In: Future generation computer systems. Springer

  17. CISCO (2015) Fog computing and the internet of things: extend the cloud to where the things are (whitepaper). [Online]

  18. UbiSpark project, http://ubispark.cs.helsinki.fi/

  19. Mohan N, Zhou P, Govindaraj K, Kangasharju J (2017) Managing data in computational edge clouds. In: Proceedings of the workshop on mobile edge communications (MECOMM ’17). ACM

  20. Xu J, Fortes JAB (2010) Multi-objective virtual machine placement in virtualized data center environments. In: Green computing and communications (GreenCom), 2010 IEEE/ACM international conference on cyber, physical and social computing (CPSCom), Hangzhou

  21. Shakshuki E, Haubenwaller AM, Vandikas K (2015) Computations on the edge in the internet of things. Procedia Comput Sci 52:29–34

    Article  Google Scholar 

  22. A quadratic assignment problem library, http://anjos.mgi.polymtl.ca/qaplib/. Accessed: 30 Sep 2010

  23. Yurko MC (2010) A parallel computational framework for solving quadratic assignment problems exactly

  24. Martello S, Minoux M, Ribeiro C, Laporte G (2011) Surveys in combinatorial optimization, vol 31. Elsevier, Amsterdam

    Google Scholar 

  25. List of power dissipation for CPUs, en.wikipedia.org/wiki/List_of_CPU_power_dissipation_figures

  26. Edge-Fog simulator and LPCF solver, https://github.com/nitinder-mohan/EdgeFogSimulator

  27. Platypus: multi-objective optimization in Python, http://platypus.readthedocs.io/en/latest/index.html

  28. Kurniawan IP, Febiansyah H, Kwon JB (2014) Cost-effective content delivery networks using clouds and nano data centers. In: Ubiquitous information technologies and applications. Springer, pp 417–424

  29. Shi C et al (2012) Serendipity: enabling remote computing among intermittently connected mobile devices. In: ACM MobiHoc

  30. Li Y, Wang W (2014) Can mobile cloudlets support mobile applications?. In: IEEE INFOCOM 2014 - IEEE conference on computer communications, pp 1060–1068

  31. Mtibaa A, Harras KA, Fahim A (2013) Towards computational offloading in mobile device clouds. In: 2013 IEEE 5th international conference on cloud computing technology and science, vol 1, pp 331–338

  32. Bonomi F et al (2014) Fog computing: a platform for internet of things and analytics. In: Big Data and Internet of Things: a roadmap for smart environments. Springer, pp 169–186

  33. Anderson T et al (2014) A brief overview of the nebula future internet architecture. SIGCOMM Comput Commun Rev 44(3):81–86

    Article  Google Scholar 

  34. Khan AM, Navarro L, Sharifi L, Veiga L (2013) Clouds of small things: provisioning infrastructure-as-a-service from within community networks. In: IEEE 9th international conference on wireless and mobile computing, networking and communications (WiMob), Lyon

  35. Silva P, Perez C, Desprez F (2016) Efficient heuristics for placing large-scale distributed applications on multiple clouds. In: 16th IEEE/ACM international symposium on cluster, cloud, and grid computing, CCGrid

  36. Ahvar E, Ahvar S, Crespi N, Garcia-Alfaro J, Mann ZÁ (2015) NACER: a network-aware cost-efficient resource allocation method for processing-intensive tasks in distributed clouds. In: IEEE 14th international symposium on network computing and applications, (NCA)

  37. Gai K et al (2016) Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. In: 2016 Elsevier Journal of Network and Computer Applications, vol 59

  38. Gai K et al (2016) Energy-aware optimal task assignment for mobile heterogeneous embedded systems in cloud computing. In: 2016 IEEE 3rd international conference on cyber security and cloud computing (CSCloud)

  39. Liu C, Qin X, Kulkarni S, Wang C, Li S, Manzanares A, Baskiyar S (2008) Distributed energy-efficient scheduling for data-intensive applications with deadline constraints on data grids. In: 2008 conference proceedings of the ieee international performance, computing, and communications conference

  40. Mao Y, Zhang J, Letaief KB Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J Sel Areas Commun

Download references

Funding

This research was funded by the joint EU FP7 Marie Curie Actions Cleansky Project, Contract No. 607584.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Helsinki, Helsinki, Finland

    Nitinder Mohan & Jussi Kangasharju

Authors
  1. Nitinder Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jussi Kangasharju
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nitinder Mohan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohan, N., Kangasharju, J. Placing it right!: optimizing energy, processing, and transport in Edge-Fog clouds. Ann. Telecommun. 73, 463–474 (2018). https://doi.org/10.1007/s12243-018-0649-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-018-0649-0

Keywords

Search

Navigation