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Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud
Summary of the article presenting GraphLab, a fault-tolerant system that provides a dynamic and asynchronous execution model for processing large-scale graphs.
Pregel: A System for Large-Scale Graph Processing
Summary of the paper presenting Pregel, Google's system for processing large Graphs using a simple computational model while being designed for efficiency and fault-tolerant execution.
Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks
Summary of the paper presenting Tachyon, the distributed file system that enables reliable data sharing at memory speed.
Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing
Summary of the paper published by University of California Berkeley researchers, presenting RDD, a distributed memory abstraction for computations on large clusters.
HDFS scalability: the limits to growth
Summary of Shvachko's article presenting Hadoop distributed file system HDFS and its scalability limitations.