Distributed Multi-Cloud Based Building Data Analytics

Cloud computing has emerged as attractive platform for computing data intensive applications. However, efficient computation of this kind of workloads requires understanding how to store, process, and analyse large volumes of data in a timely manner. Many “smart cities” applications, for instance, identify how data from building sensors can be combined together to support applications such as emergency response, energy management, etc. Enabling sensor data to be transmitted to a cloud environment for processing provides a number of benefits, such as scalability and on-demand provisioning of computational resources. In this chapter, we propose the use of a multi-layer cloud infrastructure that distributes processing over sensing nodes, multiple intermediate/gateways nodes, and large data centres. Our solution aims at utilising the pervasive computational capabilities located at the edge of the infrastructure and along the data path to reduce data movement to large data centres located “deep” into the infrastructure and perform a more efficient use of computing and network resources.

[1]  Rajkumar Buyya,et al.  High-Performance Cloud Computing: A View of Scientific Applications , 2009, 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks.

[2]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[3]  Ewa Deelman,et al.  The cost of doing science on the cloud: the Montage example , 2008, HiPC 2008.

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

[5]  Renato Figueiredo,et al.  Science Clouds: Early Experiences in Cloud Computing for Scientific Applications , 2008 .

[6]  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.

[7]  Omer F. Rana,et al.  STACEE: enhancing storage clouds using edge devices , 2011, ACE '11.

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

[9]  Ian Gorton,et al.  Exploring Architecture Options for a Federated, Cloud-Based System Biology Knowledgebase , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[10]  Madoka Yuriyama,et al.  Sensor-Cloud Infrastructure - Physical Sensor Management with Virtualized Sensors on Cloud Computing , 2010, 2010 13th International Conference on Network-Based Information Systems.

[11]  Douglas Thain,et al.  Distributed computing in practice: the Condor experience , 2005, Concurr. Pract. Exp..

[12]  Giancarlo Fortino,et al.  Managing Data and Processes in Cloud-Enabled Large-Scale Sensor Networks: State-of-the-Art and Future Research Directions , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[13]  Radu Prodan,et al.  Extending Grids with cloud resource management for scientific computing , 2009, 2009 10th IEEE/ACM International Conference on Grid Computing.

[14]  Antonio Puliafito,et al.  How to Enhance Cloud Architectures to Enable Cross-Federation , 2010, IEEE CLOUD.

[15]  W. Kurschl,et al.  Combining cloud computing and wireless sensor networks , 2009, iiWAS.

[16]  Alfredo Cuzzocrea,et al.  Exploiting compression and approximation paradigms for effective and efficient online analytical processing over sensor network readings in data grid environments , 2013, Concurr. Comput. Pract. Exp..

[17]  Domenico Saccà,et al.  Exploiting Compression and Approximation Paradigms for Effective and Efficient OLAP over Sensor Network Readings in Data Grid Environments , 2013 .

[18]  Muli Ben-Yehuda,et al.  The Reservoir model and architecture for open federated cloud computing , 2009, IBM J. Res. Dev..

[19]  Yu Xie,et al.  Federated Computing for the Masses--Aggregating Resources to Tackle Large-Scale Engineering Problems , 2014, Computing in Science & Engineering.

[20]  Rajkumar Buyya,et al.  Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters , 2009, HPDC '09.

[21]  Eduardo Huedo,et al.  Dynamic Provision of Computing Resources from Grid Infrastructures and Cloud Providers , 2009, 2009 Workshops at the Grid and Pervasive Computing Conference.

[22]  Ewa Deelman,et al.  Experiences using cloud computing for a scientific workflow application , 2011, ScienceCloud '11.

[23]  Alfredo Cuzzocrea,et al.  A Grid Framework for Approximate Aggregate Query Answering on Summarized Sensor Network Readings , 2004, OTM Workshops.

[24]  Liana L. Fong,et al.  Cloud federation in a layered service model , 2012, J. Comput. Syst. Sci..

[25]  Sergio Greco,et al.  A distributed system for answering range queries on sensor network data , 2005, Third IEEE International Conference on Pervasive Computing and Communications Workshops.

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

[27]  Carlos R. Senna,et al.  Enabling execution of service workflows in grid/cloud hybrid systems , 2010, 2010 IEEE/IFIP Network Operations and Management Symposium Workshops.

[28]  Scott Klasky,et al.  Experiments with in-transit processing for data intensive grid workflows , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.

[29]  Jyotirmay Mathur,et al.  EnergyPlus Simulation Speedup Using Data Parallelization Concept , 2010 .

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

[31]  Yacine Rezgui,et al.  A modular optimisation model for reducing energy consumption in large scale building facilities , 2014 .

[32]  Joseph M. Hellerstein,et al.  MAD Skills: New Analysis Practices for Big Data , 2009, Proc. VLDB Endow..

[33]  Djamal Zeghlache,et al.  Mathematical Programming Approach for Revenue Maximization in Cloud Federations , 2017, IEEE Transactions on Cloud Computing.

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