CS5296 Cloud Computing: Theory and Practice

Paper list

• Distributed storage

  • The Google File System

  • Dynamo: Amazon’s Highly Available Key-value Store

  • Cassandra - A Decentralized Structured Storage System

• Data analytics

  • MapReduce: Simplified Data Processing on Large Clusters

  • Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing

  • Spark SQL: Relational Data Processing in Spark

  • GraphX: Graph Processing in a Distributed Dataflow Framework

• Cluster management

  • Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center

  • Dominant Resource Fairness: Fair Allocation of Multiple Resource Types

  • Apache Hadoop YARN: Yet Another Resource Negotiator

  • Borg, Omega, and Kubernetes

• Memory systems

  • The Case for RAMClouds: Scalable High-Performance Storage Entirely in DRAM

  • Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks

  • Operating System Implications of Fast, Cheap, Non-Volatile Memory

• Workload measurements

  • Heterogeneity and Dynamicity of Clouds at Scale: Google Trace Analysis

  • Complexity vs. Performance: Empirical Analysis of Machine Learning as a Service

  • About Alibaba cluster and why we open the data

  • Inside the Social Network’s (Datacenter) Network

• Networking I: Architecture

  • OpenFlow: Enabling Innovation in Campus Networks

  • VL2: A Scalable and Flexible Data Center Network

  • The Road to SDN: An intellectual history of programmable networks

  • B4: Experience with a Globally-Deployed Software Defined WAN

  • Network Virtualization in Multi-tenant Datacenters

  • Sharing the Data Center Network

• Networking II: Performance

  • Data Center TCP (DCTCP)

  • Azure Accelerated Networking: SmartNICs in the Public Cloud

  • Andromeda: Performance, Isolation, and Velocity at Scale in Cloud Network Virtualization

  • pFabric: Minimal Near-Optimal Datacenter Transport

  • Efficient Coflow Scheduling with Varys

  • CONGA: Distributed Congestion-Aware Load Balancing for Datacenters

  • Expeditus: Congestion-Aware Load Balancing in Clos Data Center Networks

  • Congestion Control for Large-Scale RDMA Deployments

• Machine learning systems

  • Scaling Distributed Machine Learning with the Parameter Server

  • PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems

  • TensorFlow: A System for Large-Scale Machine Learning

  • TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

• Others: energy, microarchitecture, etc.

  • Temperature Management in Data Centers: Why Some (Might) Like It Hot

  • In-Datacenter Performance Analysis of a Tensor Processing Unit

  • A Configurable Cloud-Scale DNN Processor for Real-Time AI