Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

The emergence of Internet of Things (IoT) is participating to the increase of data-and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We propose an analytic model to decide whether to offload computation from the objects to the edge or to the core Cloud, depending on the renewable energy availability and the desired application QoS. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras.

[1]  Enzo Baccarelli,et al.  Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study , 2016, IEEE Network.

[2]  Nick Antonopoulos,et al.  Video Stream Analysis in Clouds: An Object Detection and Classification Framework for High Performance Video Analytics , 2019, IEEE Transactions on Cloud Computing.

[3]  Mahadev Satyanarayanan,et al.  Scalable crowd-sourcing of video from mobile devices , 2013, MobiSys '13.

[4]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[5]  Ying Gao,et al.  Quantifying the Impact of Edge Computing on Mobile Applications , 2016, APSys.

[6]  Laurent Lefèvre,et al.  A survey on techniques for improving the energy efficiency of large-scale distributed systems , 2014, ACM Comput. Surv..

[7]  Manish Parashar,et al.  Enabling autonomic computing on federated advanced cyberinfrastructures , 2013, CAC.

[8]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[9]  Tansu Alpcan,et al.  Fog Computing May Help to Save Energy in Cloud Computing , 2016, IEEE Journal on Selected Areas in Communications.

[10]  K. C. Divya,et al.  Battery Energy Storage Technology for power systems-An overview , 2009 .

[11]  Chong Luo,et al.  Multimedia Cloud Computing , 2011, IEEE Signal Processing Magazine.

[12]  Jean-Marc Menaud,et al.  Opportunistic Scheduling in Clouds Partially Powered by Green Energy , 2015, 2015 IEEE International Conference on Data Science and Data Intensive Systems.

[13]  政子 鶴岡,et al.  1998 IEEE International Conference on SMCに参加して , 1998 .

[14]  Luis Rodero-Merino,et al.  Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing , 2014, CCRV.

[15]  Christine Morin,et al.  Microcities: A Platform Based on Microclouds for Neighborhood Services , 2016, ICA3PP.

[16]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[17]  Thu D. Nguyen,et al.  Parasol and GreenSwitch: managing datacenters powered by renewable energy , 2013, ASPLOS '13.

[18]  Thomas Ledoux,et al.  Towards energy-proportional clouds partially powered by renewable energy , 2016, Computing.

[19]  Shangguang Wang,et al.  An overview of Internet of Vehicles , 2014 .

[20]  Toyotaro Suzumura,et al.  Elastic Stream Computing with Clouds , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[21]  P. Priyanga,et al.  Enabling Smart Cloud Services Through Remote Sensing : An Internet of Everything Enabler , 2015 .

[22]  Catherine Rosenberg,et al.  Toward a Realistic Performance Analysis of Storage Systems in Smart Grids , 2015, IEEE Transactions on Smart Grid.

[23]  Haisheng Chen,et al.  Progress in electrical energy storage system: A critical review , 2009 .

[24]  Khaled Ben Letaief,et al.  Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices , 2016, IEEE Journal on Selected Areas in Communications.

[25]  Emmanuel Jeannot,et al.  Adding Virtualization Capabilities to the Grid'5000 Testbed , 2012, CLOSER.

[26]  Peter Kilpatrick,et al.  Challenges and Opportunities in Edge Computing , 2016, 2016 IEEE International Conference on Smart Cloud (SmartCloud).