Unleashing the Power of Systems Simulation and Modelling for Cloud Computing and Big Data Applications

Written by renowned author Dinesh Peter, this comprehensive guidebook provides readers with cutting-edge approaches to solving complex problems using models and simulations, and presents SSM-based methods for performance testing and benchmarking that offer significant advantages.

In today’s cloud computing and big data landscape, the ability to perform tests in a controllable and repeatable manner is crucial. This book explores the development of benchmarks such as BigDataBench, BigBench, HiBench, PigMix, CloudSuite, and GridMix, designed to analyze the performance of different big data processing and cloud frameworks.

The Quest for Holistic Big Data SSM

Despite substantial progress, the research community still requires a comprehensive big data SSM to utilize in various scientific and engineering disciplines involving multidisciplinary research. SSM develops frameworks that can be applied across disciplines to create benchmarking tools that are valuable in solutions development.

Key Features and Applications

• Examines the methodology and requirements of benchmarking big data and cloud computing tools
• Covers advances in big data frameworks and benchmarks for large-scale data analytics
• Discusses frameworks for benchmarking and predictive analytics in big data deployment
• Explores applications using big data benchmarks, such as BigDataBench, BigBench, HiBench, MapReduce, HPCC, ECL, HOBBIT, GridMix, and PigMix
• Investigates applications using big data frameworks, including Hadoop, Spark, Samza, Flink, and SQL frameworks
• Delves into the development of big data benchmarks to evaluate workloads in state-of-the-practice heterogeneous hardware platforms
• Explores advances in modeling and simulation tools for performance evaluation, security problems, and scalable cloud computing environments

Publication Details

Published by Elsevier Science on February 26, 2020, this book is available in English and can be identified by the following ISBN numbers: 9780128197790 and 9780128197806.