Distributed Computing Execution Models

This lecture covers the challenges of handling large data sizes in distributed computing, focusing on the first generation of execution models. It explores the exponential growth of data, the characteristics of big data (volume, velocity, variety, variability, veracity), and declustering techniques like hash and range declustering. The lecture also discusses the tradeoffs involved in declustering and presents the Gamma Parallel Database Machine, emphasizing failure management strategies and declustering schemes like Interleaved and Chained Declustering.