End-to-end energy models for Edge Cloud-based IoT platforms: Application to data stream analysis in IoT

Future Generation Computer Systems

Volumn 87, Issue , 2018, Pages 667-678

  Li, Yunbo ^a,b   Orgerie, Anne Cécile ^a   Rodero, Ivan ^c   Amersho, Betsegaw Lemma ^a,d   Parashar, Manish ^c   Menaud, Jean Marc ^b  

^a CAMPUS DE BEAULIEU   (France)

^b INSTITUT DE RECHERCHE EN COMMUNICATIONS ET CYBERNÉTIQUE DE NANTES   (France)

^c RUTGERS UNIVERSITY   (United States)

^d AALTO UNIVERSITY   (Finland)

Author keywords

Data stream analysis; Edge Cloud computing; End to end energy model; Energy efficiency; IoT

Indexed keywords

DIGITAL STORAGE; ENERGY EFFICIENCY; ENERGY UTILIZATION; GREEN COMPUTING; INTERNET SERVICE PROVIDERS;

CLOUD INFRASTRUCTURES; COMPUTING CAPABILITY; DATA STREAM; EDGE CLOUDS; ENERGY MODEL; INTERNET OF THINGS (IOT); INTERNET SERVICE PROVIDERS (ISP); WIRELESS ACCESS POINTS;

INTERNET OF THINGS;

EID: 85039909917     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2017.12.048     Document Type: Article

References (63)

1. Ericsson, More than 50 billion connected devices, White Paper, 2011.
2. D. Evans, The Internet of Things - How the Next Evolution of the Internet Is Changing Everything, Cisco White Paper, 2011.
3. A. Nordrum, Popular Internet of Things Forecast of 50 Billion Devices by 2020 Is Outdated, IEEE Spectrum, 2016.
4. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M., Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17:4 (2015), 2347–2376.
5. S.C.B. Intelligence, Six Technologies with Potential Impacts on US Interests out to 2025, Technical Report, National Intelligent Concil, 2008.
6. Abdelwahab, S., Hamdaoui, B., Guizani, M., Rayes, A., Enabling smart cloud services through remote sensing: An internet of everything enabler. IEEE Internet Things J. 1:3 (2014), 276–288.
7. Vaquero, L.M., Rodero-Merino, L., Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Comput. Commun. Rev. 44:5 (2014), 27–32.
8. W. Hu, Y. Gao, K. Ha, J. Wang, B. Amos, Z. Chen, P. Pillai, M. Satyanarayanan, Quantifying the impact of edge computing on mobile applications, in: ACM SIGOPS Asia-Pacific Workshop on Systems, 2016, pp. 5:1–5:8.
9. B. Varghese, N. Wang, S. Barbhuiya, P. Kilpatrick, D.S. Nikolopoulos, Challenges and Opportunities in Edge Computing, CoRR abs/1609.01967.
10. T. Bawden, Global warming: Data centres to consume three times as much energy in next decade, experts warn, Independent, 2016. http://www.independent.co.uk/environment/global-warming-data-centres-to-consume-three-times-as-much-energy-in-next-decade-experts-warn-a6830086.html.
11. Goiri, Í., Katsak, W., Le, K., Nguyen, T.D., Bianchini, R., Parasol and greenswitch: Managing datacenters powered by renewable energy. ACM SIGARCH Comput. Archit. News 41:1 (2013), 51–64.
12. Xu, L.D., He, W., Li, S., Internet of things in industries: A survey. IEEE Trans. Ind. Inform. 10:4 (2014), 2233–2243.
13. Serrano, P., Garcia-Saavedra, A., Bianchi, G., Banchs, A., Azcorra, A., Per-frame energy consumption in 802. 11 devices and its implication on modeling and design. IEEE/ACM Trans. Netwing 23:4 (2015), 1243–1256.
14. H. Wu, S. Nabar, R. Poovendran, An energy framework for the network simulator 3 (NS-3), in: International Conference on Simulation Tools and Techniques, SIMUTools, 2011, pp. 222–230.
15. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M., Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17:4 (2015), 2347–2376.
16. Islam, S., Grégoire, J.-C., Network edge intelligence for the emerging next-generation internet. Future Internet 2:4 (2010), 603–623.
17. Y. Li, A.-C. Orgerie, I. Rodero, M. Parashar, J.-M. Menaud, Leveraging renewable energy in edge Clouds for data stream analysis in IoT, in: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid, 2017, pp. 186–195.
18. L. Rosyidi, R.F. Sari, Energy harvesting aware protocol for 802.11-based internet of things network, in: IEEE Region 10 Conference, TENCON, 2016, pp. 1325–1328.
19. Y.W. Kuo, C.L. Li, Design of long range low power sensor node for the last mile of IoT, in: IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW, 2016, pp. 1–2.
20. Jalali, F., Hinton, K., Ayre, R., Alpcan, T., Tucker, R.S., Fog computing may help to save energy in cloud computing. IEEE J. Sel. Areas Commun. 34:5 (2016), 1728–1739.
21. M. Kurpicz, A.-C. Orgerie, A. Sobe, How much does a VM cost? Energy-proportional accounting in VM-based environments, in: Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP, 2016, pp. 651–658.
22. Yang, F., Wang, S., Li, J., Liu, Z., Sun, Q., An overview of internet of vehicles. China Commun. 11:10 (2014), 1–15.
23. Ishii, A., Suzumura, T., Elastic stream computing with clouds. 2011 IEEE International Conference on Cloud Computing (CLOUD), 2011, IEEE, 195–202.
24. Baccarelli, E., Cordeschi, N., Mei, A., Panella, M., Shojafar, M., Stefa, J., Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: Review, challenges, and a case study. IEEE Netw. 30:2 (2016), 54–61.
25. S. Nedevschi, L. Popa, G. Iannaccone, S. Ratnasamy, D. Wetherall, Reducing network energy consumption via sleeping and rate-adaptation, in: USENIX Symposium on Networked Systems Design and Implementation, NSDI, vol. 8, 2008, pp. 323–336.
26. W. Fisher, M. Suchara, J. Rexford, Greening backbone networks: Reducing energy consumption by shutting off cables in bundled links, in: ACM SIGCOMM workshop on Green networking, 2010, pp. 29–34.
27. Figuerola, S., Lemay, M., Reijs, V., Savoie, M., Arnaud, B.St., Converged optical network infrastructures in support of future internet and grid services using IaaS to reduce GHG emissions. J. Lightwave Technol. 27:12 (2009), 1941–1946.
28. Zhu, W., Luo, C., Wang, J., Li, S., Multimedia cloud computing. IEEE Signal Process. Mag. 28:3 (2011), 59–69.
29. Bertier, M., Desprez, F., Fedak, G., Lebre, A., Orgerie, A.-C., Pastor, J., Quesnel, F., Rouzaud-Cornabas, J., Tedeschi, C., Beyond the clouds: How should next generation utility computing infrastructures be designed?. Cloud Computing, Computer Communications and Networks, 2014, Springer, 325–345.
30. Satyanarayanan, M., The emergence of edge computing. Computer 50:1 (2017), 30–39.
31. P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, vol. 1, 2001, pp. 511–518.
32. Schapire, R.E., Singer, Y., Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37:3 (1999), 297–336.
33. J. Koomey, Growth in data center electricity use 2005 to 2010, White paper, 2011.
34. Schmidt, R., Beaty, D., Dietrich, J., Increasing energy efficiency in data centers. ASHRAE J., 2007, 18–24.
35. J. Whitney, P. Delforge, Data center efficiency assessment scaling up energy efficiency across the data center industry: Evaluating key drivers and Barriers, NRDC White Paper, 2014.
36. K. Church, A.G. Greenberg, J.R. Hamilton, On delivering embarrassingly distributed cloud services., in: HotNets, 2008, pp. 55–60.
37. A. Shehabi, S. Smith, N. Horner, I. Azevedo, R. Brown, J. Koomey, E. Masanet, D. Sartor, M. Herrlin, W. Lintner, United states data center energy usage report, Lawrence Berkeley National Laboratory, Berkeley, California. LBNL-1005775 Page 4.
38. C. Belady, A. Rawson, J. Pfleuger, T. Cader, Green grid data center power efficiency metrics: PUE and DCIE, Technical Report, Green Grid, 2008.
39. Green Grid, Harmonizing global metrics for data center energy efficiency, global taskforce reaches agreement regarding data center productivity, Technical Report, The Green Grid Consortium, 2014.
40. M. Chowdhury, M. Zaharia, J. Ma, M. Jordan, I. Stoica, Managing data transfers in computer clusters with orchestra, in: SIGCOMM, 2011, pp. 98–109.
41. Orgerie, A.-C., Dias de Assunção, M., Lefèvre, L., A survey on techniques for improving the energy efficiency of large-scale distributed systems. ACM Comput. Surv. (CSUR), 46(4), 2014, 47.
42. Bolla, R., Bruschi, R., Davoli, F., Cucchietti, F., Energy efficiency in the future internet: A survey of existing approaches and trends in energy-aware fixed network infrastructures. IEEE Commun. Surv. Tutor. 13:2 (2011), 223–244.
43. Tsai, C.W., Lai, C.F., Chiang, M.C., Yang, L.T., Data mining for internet of things: A survey. IEEE Commun. Surv. Tutor. 16:1 (2014), 77–97.
44. B.B.P. Rao, P. Saluia, N. Sharma, A. Mittal, S.V. Sharma, Cloud computing for Internet of Things amp; sensing based applications, in: International Conference on Sensing Technology, ICST, 2012, pp. 374–380.
45. P. Simoens, Y. Xiao, P. Pillai, Z. Chen, K. Ha, M. Satyanarayanan, Scalable crowd-sourcing of video from mobile devices, in: ACM International Conference on Mobile Systems, Applications, and Services, 2013, pp. 139–152.
46. https://www.ffmpeg.org.
47. Diaz-Montes, J., Zou, M., Rodero, I., Parashar, M., Enabling autonomic computing on federated advanced cyberinfrastructures. ACM Cloud and Autonomic Computing Conference, 2013, ACM, 20.
48. M. Ergen, P. Varaiya, Decomposition of energy consumption in IEEE 802.11, in: IEEE International Conference on Communications, ICC, 2007, pp. 403–408.
49. D. Halperin, B. Greenstein, A. Sheth, D. Wetherall, Demystifying 802.11n power consumption, in: International Conference on Power Aware Computing and Systems, HotPower, 2010, pp. 1–5.
50. D-Link, DCS-2330L HD Wireless N Day/Night Outdoor Cloud Camera, Datasheet, 2014. http://www.dlink.com/uk/en/-/media/consumer_products/dcs/dcs-2330l/datasheet/dcs_2330l_datasheet_deu.pdf.
51. Libelium, Waspmote technical guide, 2017. http://www.libelium.com/downloads/documentation/waspmote_technical_guide.pdf.
52. G. Auer, et al., Energy efficiency analysis of the reference systems, areas of improvements and target breakdown, Tech. rep., European Project NFSO-ICT-247733 EARTH - Deliverable D2.3 2012.
53. Deruyck, M., Vereecken, W., Joseph, W., Lannoo, B., Pickavet, M., Martens, L., Reducing the power consumption in wireless access networks: Overview and recommendations. Prog. Electromagn. Res. 132 (2012), 255–274.
54. I. Cuadrado-Cordero, F. Cuadrado, C. Phillips, A.-C. Orgerie, C. Morin, Microcities: A platform based on microclouds for neighborhood services, in: International Conference Algorithms and Architectures for Parallel Processing, ICA3PP, 2016, pp. 192–202.
55. A. Shehabi, et al., United States Data Center Energy Usage Report, Technical Report, Lawrence Berkeley National Laboratory LBNL-1005775, 2016.
56. A. Anjum, T. Abdullah, M. Tariq, Y. Baltaci, N. Antonopoulos, Video stream analysis in clouds: An object detection and classification framework for high performance video analytics, IEEE Transactions on Cloud Computing.
57. Y. Mao, J. Zhang, K.B. Letaief, Dynamic computation offloading for mobile-edge computing with energy harvesting devices, arXiv preprint arXiv:1605.05488.
58. Beldiceanu, N., Dumas Feris, B., Gravey, P., Hasan, S., Jard, C., Ledoux, T., Li, Y., Lime, D., Madi-Wamba, G., Menaud, J.-M., Morel, P., Morvan, M., Moulinard, M.-L., Orgerie, A.-C., Pazat, J.-L., Roux, O.H., Sharaiha, A., Towards energy-proportional Clouds partially powered by renewable energy. Computing, 2016, 20.
59. ns3 network simulator, http://www.nsnam.org/ns-3-11/.
60. Y. Li, A.-C. Orgerie, J.-M. Menaud, Opportunistic scheduling in clouds partially powered by green energy, in: GreenCom: IEEE International Conference on Green Computing and Communications, 2015, pp. 448–455.
61. V. Shrivastava, S. Rayanchu, J. Yoonj, S. Banerjee, 802.11n under the microscope, in: ACM SIGCOMM Conference on Internet Measurement, IMC, 2008, pp. 105–110.
62. Balouek, D., et al. Adding virtualization capabilities to the grid'5000 testbed. Cloud Computing and Services Science, vol. 367, 2013, Springer, 3–20.
63. I. Petri, J. Diaz-Montes, M. Zou, O.F. Rana, T. Beach, H. Li, Y. Rezgui, In-transit data analysis and distribution in a multi-cloud environment using CometCloud, in: International Conference on Future Internet of Things and Cloud, FiCloud, 2014, pp. 471–476.