Academic Press Systems Simulation and Modeling for Cloud Computing and Big Data Applications (Volume 10) (Advances in ubiquitous sensing applications for healthcare, Volume 10)

Product Description Systems Simulation and Modelling for Cloud Computing and Big Data Applications provides readers with the most current approaches to solving problems through the use of models and simulations, presenting 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. Inspired 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 all covered in this book. Despite the substantial progress, the research community still needs a holistic, comprehensive big data SSM to use 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. Review A comprehensive guide to systems simulation and modeling for multidisciplinary scientific and engineering research and applications From the Back Cover 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. About the Author 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 D