AMPLab Publications

• Matrix Computations and Optimization in Apache Spark
• SparkR: Scaling R Programs with Spark
• MLlib: Machine Learning in Apache Spark
• FairRide: Near-Optimal, Fair Cache Sharing
• Spark SQL: Relational Data Processing in Spark
• Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks
• Discretized Streams: Fault-Tolerant Streaming Computation at Scale
• Tachyon: Memory Throughput I/O for Cluster Computing Frameworks
• Sparrow: Distributed, Low Latency Scheduling
• Large Scale Estimation in Cyberphysical Systems using Streaming Data: a Case Study with Smartphone Traces
• Scaling the Mobile Millennium System in the Cloud
• Choosy: Proportional Sharing for Datacenter Jobs with Constraints
• Fast and Interactive Analytics over Hadoop Data with Spark
• Shark: SQL and Rich Analytics at Scale
• Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters
• Faster and More Accurate Sequence Alignment with SNAP
• Shark: Fast Data Analysis Using Coarse-grained Distributed Memory (Best Demo Award)
• A Common Substrate for Cluster Computing
• Improving MapReduce Performance in Heterogeneous Environments
• Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing (Best Paper Award)
• Scaling the Mobile Millennium System in the Cloud
• The Datacenter Needs an Operating System
• Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center
• Dominant Resource Fairness: Fair Allocation of Multiple Resources Types
• Spark: Cluster Computing with Working Sets
• Managing Data Transfers in Computer Clusters with Orchestra