Strategies to Implement Edge Computing in a P2P Pervasive Grid

The exponential dissemination of proximity computing devices (smartphones, tablets, nanocomputers, etc.) raises important questions on how to transmit, store and analyze data in networks integrating those devices. New approaches like edge computing aim at delegating part of the work to devices in the “edge” of the network. In this article, the focus is on the use of pervasive grids to implement edge computing and leverage such challenges, especially the strategies to ensure data proximity and context awareness, two factors that impact the performance of big data analyses in distributed systems. This article discusses the limitations of traditional big data computing platforms and introduces the principles and challenges to implement edge computing over pervasive grids. Finally, using CloudFIT, a distributed computing platform, the authors illustrate the deployment of a real geophysical application on a pervasive network.

[1]  Alex Wright.  Big data meets big science , 2014, CACM.

[2]  A. Schuch,et al.  Measurements of the total ozone column using a Brewer spectrophotometer and TOMS and OMI satellite instruments over the Southern Space Observatory in Brazil , 2017 .

[3]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[4]  Manuele Kirsch-Pinheiro,et al.  Improving the performance of Apache Hadoop on pervasive environments through context-aware scheduling , 2016, J. Ambient Intell. Humaniz. Comput..

[5]  Manuele Kirsch-Pinheiro,et al.  Using a Pervasive Computing Environment to Identify Secondary Effects of the Antarctic Ozone Hole , 2016, ANT/SEIT.

[6]  Kam-Wing Ng,et al.  Aurelia: Building Locality-Preserving Overlay Network over Heterogeneous P2P Environments , 2005, ISPA Workshops.

[7]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

[8]  Manuele Kirsch-Pinheiro,et al.  When the Cloud Goes Pervasive: Approaches for IoT PaaS on a Mobiquitous World , 2015, IoT 360.

[9]  Jean-Marc Pierson,et al.  Pervasive Grids Challenges and Opportunities , 2008 .

[10]  David P. Anderson,et al.  SETI@home: an experiment in public-resource computing , 2002, CACM.

[11]  Colette Johnen,et al.  Self-stabilization versus Robust Self-stabilization for Clustering in Ad-Hoc Network , 2011, Euro-Par.

[12]  Unai Arronategui,et al.  A Highly Scalable Decentralized Scheduler of Tasks with Deadlines , 2011, 2011 IEEE/ACM 12th International Conference on Grid Computing.

[13]  Ovsei Gelman,et al.  The Case for Conceptual Research in Information Systems , 2008 .

[14]  Teruo Higashino,et al.  Edge-centric Computing: Vision and Challenges , 2015, CCRV.

[15]  A. O'Neill,et al.  On the motion of air through the stratospheric polar vortex , 1994 .

[16]  Claus Pahl,et al.  Containers and Clusters for Edge Cloud Architectures -- A Technology Review , 2015, 2015 3rd International Conference on Future Internet of Things and Cloud.

[17]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[18]  Lei Ying,et al.  Decentralized scheduling with data locality for data-parallel computation on peer-to-peer networks , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[19]  Francisco Vilar Brasileiro,et al.  Bridging the High Performance Computing Gap: the OurGrid Experience , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[20]  Remzi Seker,et al.  Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook , 2016, Comput. Ind..

[21]  David P. Anderson,et al.  BOINC: a system for public-resource computing and storage , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[22]  J. Farman,et al.  Large losses of total ozone in Antarctica reveal seasonal ClOx/NOx interaction , 1985, Nature.

[23]  M. Salby,et al.  Changes of the Antarctic ozone hole: Controlling mechanisms, seasonal predictability, and evolution , 2012 .

[24]  S. Bekki,et al.  Model Simulations of the Impact of the 2002 Antarctic Ozone Hole on the Midlatitudes , 2005 .

[25]  Yolande Berbers,et al.  Enabling Self-learning in Dynamic and Open IoT Environments , 2014, ANT/SEIT.

[26]  Aniruddha S. Gokhale,et al.  Dynamic Resource Management Across Cloud-Edge Resources for Performance-Sensitive Applications , 2017, 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[27]  Michaël Krajecki,et al.  Solving the Langford problem in parallel , 2004, Third International Symposium on Parallel and Distributed Computing/Third International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks.

[28]  Jignesh M. Patel,et al.  Big data and its technical challenges , 2014, CACM.

[29]  Arijit Mukherjee,et al.  Challenges of Using Edge Devices in IoT Computation Grids , 2013, ICPADS 2013.

[30]  Wen-Jang Jih,et al.  Effects of Knowledge Management Implementation in Hospitals: An Exploratory Study in Taiwan , 2006, Int. J. Knowl. Manag..

[31]  Hadina Habil,et al.  Nursing as a Global Career: Meeting the Challenges of the Profession from a Language for Specific Purposes (LSP) Perspective , 2016, Int. J. Knowl. Based Organ..

[32]  Denis Conan,et al.  Distributed event-based system with multiscoping for multiscalability , 2014, MW4NG '14.

[33]  Nelson Jorge Schuch,et al.  A study of the anticorrelations between ozone and UV-B radiation using linear and exponential fits in southern Brazil , 2004 .