CS 744: Big Data Systems

CS 744: Big Data Systems
Spring '19: Course Home Page

Overview, Architecture

[Architecture: Compute+Overall (B+09)] The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines , L.A. Barroso, U. Holzle, Synthesis Lectures on Computer Architecture, 2009. Chapter 1 and 2.
[Architecture: Networks: Optional (G+09)] VL2: A Scalable and Flexible Data Center Network, Greenberg et al., SIGCOMM 2009.
[Architecture: Storage (S+10)] The Hadoop Distributed File System, Schvachko et al, MSST, 2010.
[Architecture: Storage: Optional (GG+10)] The Google File System, Ghemawat et al, SOSP, 2003.

Execution Engines, Resource Negotiators, Schedulers

[Execution: MR (G+04)] MapReduce Simplified Data Processing on Large Clusters, Dean and Ghemawat, OSDI, 2004.
[Execution: Stragglers (A+10)] Reining in the Outliers in Map-Reduce Clusters using Mantri, Ananthanarayanan et al, OSDI, 2010.
[ResourceNeg: YARN (V+13)] Apache Hadoop YARN: Yet Another Resource Negotiator, Vavilapalli et al, SOCC, 2013.
[ResourceNeg: Mesos: Optional (H+11)] Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center, Hindman et al, NSDI, 2011.
[ResourceNeg: DRF (G+11)] Dominant Resource Fairness: Fair Allocation of Multiple Resource Types, Ghodsi et al, NSDI, 2011.
[Scheduling: Packing: Optional (G+14)] Multi-Resource Packing for Cluster Schedulers, Grandl et al, SIGCOMM, 2014.
[Execution: Spark (ZC+10)] Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, Zaharia et al, NSDI, 2012.
[Execution: Tez: Optional (SS+15)] Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications, Saha et al, SIGMOD, 2015.
[Execution: F2: Optional (G+17)] Fast and Flexible Analytics with F2, Grandl et al, 2017.

Applications: Batch Analytics

[SQL: SparkSQL (A+15)] Spark SQL: Relational Data Processing in Spark, Armburst et al, SIGMOD, 2015.
[SQL: Hive: Optional (H+14)] Major technical advancements in Apache Hive, Huai et al, SIGMOD, 2014.
[GeoDistributed: Clarinet (VA+16)] Clarinet: WAN-Aware Optimization for Analytics Queries, Viswanathan et al, OSDI, 2016.
[Dynamic Query Re-Planning using QOOP (KM+18)] Dynamic Query Re-Planning using QOOP, Kshiteej Mahajan; Mosharaf Chowdhury; Aditya Akella and Shuchi Chawla
[GeoDistributed: Geode: Optional (VCB+15)] Global Analytics in the Face of Bandwidth and Regulatory Constraints, Vulimiri et al, NSDI, 2015.

Applications: Stream Analytics

[Streaming: Storm (T+14)] Storm @Twitter , Toshniwal et al, SIGMOD, 2014.
[Streaming: Heron Optional (KB+15)] Twitter Heron: Stream Processing at Scale, Kulkarni et al, SIGMOD, 2015.
[Streaming: SparkStreaming (ZD+13)] Discretized Streams: Fault-Tolerant Streaming Computation at Scale, Zaharia et al, SOSP, 2013. Also read this introduction to Structured Streaming.
[Streaming: Flink: Optional (C+15)] Apache Flink: Stream and Batch Processing in a Single Engine, Carbone et al, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2015.
[QMS: Kafka: Optional (K+11)] Kafka Distributed Messaging System for Log Processing, Kreps et al, NetDB Workshop, 2011. Also read this comparison of widely used Queuing Messaging Processing Systems.
[Streaming: rStreams (L+16)] StreamScope: Continuous Reliable Distributed Processing of Big Data Streams, Lin et al, NSDI, 2016.

Applications: Graph Processing

[GraphProc:Pregel (M+10)] Pregel: A System for Large-Scale Graph Processing, Malewicz et al, SIGMOD, 2010.
[GraphProc: PowerGraph: Optional (GL+12)] PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs, Gonzalez et al, OSDI, 2012.
[GraphProc: GraphX (G+14)] GraphX: Graph Processing in a Distributed Dataflow Framework, Gonzalez et al, OSDI, 2014.
[Streaming/Execution: Naiad: Optional (M+13)] Naiad: A Timely Dataflow System, Murray et al, SOSP, 2013.

Applications: Machine Learning

[ML: ParamServ (LA+14)] Scaling Distributed Machine Learning with the Parameter Server, Li et al, OSDI, 2014.
[ML: STRADS Optional (K+16)] STRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning, Kim et al, EuroSys, 2016.
[ML: SLAQ (Z+17)] SLAQ: Quality-Driven Scheduling for Distributed Machine Learning, Zhang et al, SoCC, 2017.
[ML: TensorFlow (AB+16)] TensorFlow: A System for Large-Scale Machine Learning, Abadi et al, OSDI, 2016.
[ML: Gandiva Optional (X+18)] Gandiva: Introspective Cluster Scheduling for Deep Learning, Xiao et al, OSDI, 2018.
[ML: Clipper (C+17)] Clipper: A Low-Latency Online Prediction Serving System, Crankshaw et al, NSDI, 2017.
[RL: Ray Optional (M+18)] Ray: A Distributed Framework for Emerging AI Applicationss, Moritz et al, OSDI, 2018.

Potpourri: Hardware, Serverless and Approximation

[Serverless: PyWren (J+17)] Occupy the Cloud: Distributed Computing for the 99%, Jonas et al, SoCC, 2017.
[Approx: BlinkDB (AM+13)] BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data, Agarwal et al, Eurosys, 2013.
[Hardware: TPU: Optional: (J+17)] In-Datacenter Performance Analysis of a Tensor Processing Unit, Jouppi et al, CIDR, 2017.