As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis software. Addressing performance issues, Cloud Computing: Data-Intensive Computing and Scheduling explores the evolution of classical techniques and describes completely new methods and innovative algorithms. The book delineates many concepts, models, methods, algorithms, and software used in cloud computing.
After a general introduction to the field, the text covers resource management, including scheduling algorithms for real-time tasks and practical algorithms for user bidding and auctioneer pricing. It next explains approaches to data analytical query processing, including pre-computing, data indexing, and data partitioning. Applications of MapReduce, a new parallel programming model, are then presented. The authors also discuss how to optimize multiple group-by query processing and introduce a MapReduce real-time scheduling algorithm.
A useful reference for studying and using MapReduce and cloud computing platforms, this book presents various technologies that demonstrate how cloud computing can meet business requirements and serve as the infrastructure of multidimensional data analysis applications.
Overview of Cloud Computing. Resource Scheduling for Cloud Computing. Game Theoretical Allocation in a Cloud Datacenter. Multidimensional Data Analysis in a Cloud Datacenter. Data-Intensive Applications with MapReduce. Large-Scale Multidimensional Data Aggregation. Multidimensional Data Analysis Optimization. Real-Time Scheduling with MapReduce. Future for Cloud Computing. Bibliography. Index.