Systems Simulation and Modeling for Cloud Computing and Big Data Applications: 10 (Advances in ubiquitous sensing applications for healthcare) - Softcover

Über die Autorinnen und Autoren

J. Dinesh Peter is Program Coordinator for the Department of Computer Sciences Technology at Karunya University, and author of more than 25 academic articles/chapters/conference papers. He has been active in government and industry as the developer of new technologies including Digital Image Processing, Virtual Reality Technology, Medical Image Processing, Computer Vision, and Optimization. He has been Guest Editor of a special issue of the Elsevier journal Computers and Electrical Engineering, and Guest Editor of special issues of the Journal of Cloud Computing and Journal of Big Data Intelligence. Dr. Peter received his Ph.D. in Computer Science and Engineering from National Institute of Technology Calicut, India.

Post-Doctoral Research, Department of Electrical and Computer Engineering , University of Alabama at Birmingham, USA
Ph.D. in Computer Vision & Machine Learning, Karunya University, Coimbatore, Tamil Nadu, India
Master of Technology, Microelectronics, Manipal Institute of Technology, Manipal, Karnataka, India
Bachelor of Engineering, Electronics and Communication Engineering,
Canara Engineering College, Bantwal, Karnataka, India
December 2017
March 2017
June 2011
June 2008
ACADEMIC AND TECHNICAL WORK EXPERIENCE
● Assistant Professor, Department of Electronics and Communication Engineering, Sahyadri College of Engineering and Management, India, June 2014 – June 2017
● Senior Software Test Analysts, Perform Group Pvt. Ltd., India April 2011 – June 2014

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Systems Simulation and Modelling (SSM) for Cloud Computing and Big Data Applications provides readers with the most current approaches to solving problems through the use of models and simulations. SSM is used in almost every scientific and engineering discipline involving multidisciplinary research. SSM develops frameworks that are applicable across disciplines to develop benchmarking tools that are useful in solutions development. For SSM to grow and continue to develop, modeling theories need to be transformed into consistent frameworks which in turn are implemented into consistent benchmarks. The world is clearly in the era of big data and cloud computing. The challenge for big data is balancing operation and cost tradeoffs by optimizing configurations at both the hardware and software layers to accommodate users constraints. Conducting such a study in real time computing environment can be difficult for the following reasons: establishing or renting a large-scale datacenter resource pool, frequently changing experiment configurations in a large-scale real workplace involves a lot of manual configuration, comprising and controlling different types of failure behaviors and benchmarks across heterogeneous software and hardware resource types in a real workplace.

Systems Simulation and Modelling (SSM) for Cloud Computing and Big Data Applications shows you SSM based approaches to performance testing and benchmarking that offer significant advantages. For example, multiple big data and cloud application developers and researchers can perform tests in a controllable and repeatable manner. Fired by the need to analyze the performance of different big data processing and cloud frameworks, researchers have introduced several benchmarks, including BigDataBench, BigBench, HiBench, PigMix, CloudSuite and GridMix, which are covered in this book. Despite the substantial progress, the research community still needs a holistic comprehensive big data and cloud simulation platform for different applications, covered in Systems Simulation and Modelling (SSM) for Cloud Computing and Big Data Applications.