Integrating Software Defined Networks within a Cloud Federation

Cloud computing has generally involved the use of specialist data centres to support computation and data storage at a central site (or a limited number of sites). The motivation for this has come from the need to provide economies of scale (and subsequent reduction in cost) for supporting large scale computation for multiple user applications over (generally) a shared, multi-tenancy infrastructure. The use of such infrastructures requires moving data to a central location (data may be pre-staged to such a location prior to processing using terrestrial delivery channels and does not always require the use of a network-based transfer), undertaking processing on the data, and subsequently enabling users to download results of analysis. We extend this model using software defined networks (SDNs), whereby capability within the network can be used to support in-transit processing while data is in movement from source to destination. Using a smart building infrastructure scenario, consisting of sensors and actuators embedded within a built environment, we describe how an SDN-based architecture can be used to support real time data processing. This significantly influences the processing times to support energy optimisation of the building and reduces costs. We describe an architecture for such a distributed, multi-layered Cloud system and discuss a prototype that has been implemented using the CometCloud system, deployed across three sites in the UK and the US. Wevalidate the prototype using data from sensors within a Sports facility and making use of EnergyPlus.

[1]  Nick Feamster,et al.  Improving network management with software defined networking , 2013, IEEE Commun. Mag..

[2]  Nelson Fumo,et al.  Methodology to estimate building energy consumption using EnergyPlus Benchmark Models , 2010 .

[3]  Nicholas Carriero,et al.  Linda in context , 1989, CACM.

[4]  Manish Parashar,et al.  CometCloud: Enabling Software-Defined Federations for End-to-End Application Workflows , 2015, IEEE Internet Computing.

[5]  Scott Klasky,et al.  Examples of in transit visualization , 2011, PDAC '11.

[6]  Baskar Ganapathysubramanian,et al.  Exploring the Use of Elastic Resource Federations for Enabling Large-Scale Scientific Workflows , 2013 .

[7]  Michael A. Gerber,et al.  EnergyPlus Energy Simulation Software , 2014 .

[8]  Zhen Li,et al.  A computational infrastructure for grid-based asynchronous parallel applications , 2007, HPDC '07.

[9]  Yashar Ganjali,et al.  HyperFlow: A Distributed Control Plane for OpenFlow , 2010, INM/WREN.

[10]  Martín Casado,et al.  Onix: A Distributed Control Platform for Large-scale Production Networks , 2010, OSDI.

[11]  Chin Guok,et al.  Software-Defined Networking for Big-Data Science - Architectural Models from Campus to the WAN , 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

[12]  Fan Zhang,et al.  Combining in-situ and in-transit processing to enable extreme-scale scientific analysis , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[13]  David Walker,et al.  Consistent updates for software-defined networks: change you can believe in! , 2011, HotNets-X.

[14]  Arie Shoshani,et al.  In situ data processing for extreme-scale computing , 2011 .

[15]  Kenneth Moreland,et al.  Sandia National Laboratories , 2000 .

[16]  Jeremy S. Meredith,et al.  Parallel in situ coupling of simulation with a fully featured visualization system , 2011, EGPGV '11.

[17]  Shantenu Jha,et al.  Exploring the Performance Fluctuations of HPC Workloads on Clouds , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[18]  Allan Tweed,et al.  An Analysis of the Energy and Cost Savings Potential of Occupancy Sensors for Commercial Lighting Systems , 2001 .

[19]  Yacine Rezgui,et al.  In-Transit Data Analysis and Distribution in a Multi-cloud Environment Using CometCloud , 2014, 2014 International Conference on Future Internet of Things and Cloud.

[20]  Manish Parashar,et al.  Cloud Paradigms and Practices for Computational and Data-Enabled Science and Engineering , 2013, Computing in Science & Engineering.

[21]  Manish Parashar,et al.  Exploring Models and Mechanisms for Exchanging Resources in a Federated Cloud , 2014, 2014 IEEE International Conference on Cloud Engineering.

[22]  Scott Klasky,et al.  The Center for Plasma Edge Simulation Workflow Requirements , 2006, 22nd International Conference on Data Engineering Workshops (ICDEW'06).

[23]  Yacine Rezgui,et al.  Cloud Supported Building Data Analytics , 2014, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.