Contents

• Paper Insights #1 - Moving Beyond End-to-End Path Information to Optimize CDN Performance
• Affiliation: Google
• Learning Goals: Content Delivery Networks

• Paper Insights #2 - A Comparison of Software and Hardware Techniques for x86 Virtualization
• Affiliation: VMWare
• Learning Goals: Microprocessors, Instruction Sets, Virtual Memory, Classical Virtualization, Software Virtualization, Hardware Virtualization

• Paper Insights #3 - Memory Resource Management in VMware ESX Server
• Affiliation: VMWare
• Learning Goals: Memory, Virtual Memory, Virtualized Memory

• Paper Insights #4 - Firecracker: Lightweight Virtualization for Serverless Applications
• Affiliation: Amazon Web Services
• Learning Goals: Serverless, OS-level Virtualization, Lambda, Cloud Security

• Paper Insights #5 - The Design and Implementation of a Log-Structured File System
• Affiliation: University of California, Berkeley
• Learning Goals: File, File Systems, FAT File System, Log-Structured File System

• Paper Insights #6 - F2FS: A New File System for Flash Storage
• Affiliation: Samsung
• Learning Goals: Flash Memory, Solid State Drives

• Paper Insights #7 - Rethink the Sync
• Affiliation: University of Michigan
• Learning Goals: Inter-Process Communication, Speculative Execution, Journaling File System, Commit Buffering, File System Consistency

• Paper Insights #8 - The Google File System
• Affiliation: Google
• Learning Goals: Network File System, Distributed File System

• Paper Insights #9 - CacheSack: Admission Optimization for Google Datacenter Flash Caches
• Affiliation: Google
• Learning Goals: Distributed Flash Cache

• Paper Insights #10 - MapReduce: Simplified Data Processing on Large Clusters
• Affiliation: Google
• Learning Goals: Batch Processing, MapReduce, Shuffle

• Paper Insights #11 - Apache Flink™: Stream and Batch Processing in a Single Engine 
• Affiliation: Apache 
• Learning Goals: Stream Processing, Data Flow Graph

• Paper Insights #12 - The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing
• Affiliation: Google
• Learning Goals: Stream Processing, Event-Time Processing

• Paper Insights #13 - Delta Lake: High Performance ACID Table Storage over Cloud Object Stores
• Affiliation: Databricks
• Learning Goals: Databases, ETL, Data Storage Formats, Cloud Data Stores

• Paper Insights #14 - Bigtable: A Distributed Storage System for Structured Data
• Affiliation: Google
• Learning Goals: SQL v/s NoSQL Databases, B-Trees, Log-Structured Merge Trees, Distributed Wide-Column NoSQL Database, Bloom Filters

• Paper Insights #15 - Dynamo: Amazon's Highly Available Key-value Store
• Affiliation: Amazon
• Learning Goals: Distributed Hash Tables, Consistent Hashing, Quorum Systems, Vector Clocks, Merkle Trees, Distributed Key-Value Database

• Paper Insights #16 - Cassandra - A Decentralized Structured Storage System
• Affiliation: Facebook
• Learning Goals: Failure Detectors, Gossip Protocols

• Paper Insights #17 - The Eternal Tussle: Exploring the Role of Centralization in IPFS
• Affiliation: Protocol Labs
• Learning Goals: Kademlia Protocol, InterPlanetary File System

• Paper Insights #18 - Practical Uses of Synchronized Clocks in Distributed Systems
• Affiliation: Massachusetts Institute of Technology
• Learning Goals: Clock Synchronization, Network Time Protocol, At Most Once Delivery, Authentication Tokens, Cache Consistency, External Consistency

• Paper Insights #19 - Kafka: A Distributed Messaging System for Log Processing
• Affiliation: LinkedIn
• Learning Goals: Message Queues, PubSub, At Least Once Delivery

• Paper Insights #20 - Paxos Made Simple 
• Affiliation: Massachusetts Institute of Technology
• Learning Goals: Consensus, FLP Impossibility, Paxos, Leader Election, Distributed Logs

• Paper Insights #21 - ZooKeeper: Wait-free coordination for Internet-scale systems
• Affiliation: Yahoo
• Learning Goals: Distributed Logs, Consistency Models, Linearizability, Sequential Consistency, Consensus, Atomic Broadcast, Coordination Kernel, Leader Election, Distributed Locks

• Paper Insights #22 - A New Presumed Commit Optimization for Two Phase Commit
• Affiliation: DEC Research Lab
• Learning Goals: Distributed Transactions, 2PC, 3PC

• Paper Insights #23 - Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases
• Affiliation: Amazon Web Services
• Learning Goals: Correlated Failures, Replication, Distributed SQL Database

• Paper Insights #24 - Spanner: Google's Globally-Distributed Database
• Affiliation: Google
• Learning Goals: CAP Theorem, Distributed SQL Database, Strict Serializability, TrueTime, Paxos, 2PC

• Paper Insights #25 - Eliminating Receive Livelock in an Interrupt-driven Kernel
• Affiliation: Digital Equipment Corporation 
• Learning Goals: I/O, Networking, Rate Limiting

• Paper Insights #26 - CliqueMap: Productionizing an RMA-Based Distributed Caching System
• Affiliation: Google
• Learning Goals: Software RDMA, In-Memory Caching

• Paper Insights #27 - Don't Settle for Eventual: Scalable Causal Consistency for Wide-Area Storage with COPS
• Affiliation: Princeton University
• Learning Goals: Logical Clocks, Causal Consistency

• Paper Insights #28 - TAO: Facebook's Distributed Data Store for the Social Graph
• Affiliation: Facebook
• Learning Goals: Eventual Consistency, Distributed Graph Databases

• Paper Insights #29 - Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center
• Affiliation: University of California, Berkeley
• Learning Goals: Cluster Computing, High-Performance Computing, Task Scheduling

• Paper Insights #30 - Autopilot: Workload Autoscaling at Google Scale
• Affiliation: Google
• Learning Goals: Scaling

• Paper Insights #31 - F1 Query: Declarative Querying at Scale
• Affiliation: Google
• Learning Goals: Query Processing, Big Data

• Paper Insights #32 - Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google
• Affiliation: Google
• Learning Goals: Data Warehouse, Distributed Analytical Database

• Paper Insights #33 - Photon: Fault-tolerant and Scalable Joining of Continuous Data Streams
• Affiliation: Google
• Learning Goals: Streaming Joins

• Paper Insights #34 - CRDTs: Consistency without Concurrency Control
• Affiliation: National Institute for Research in Computer Science and Automation
• Learning Goals: Commutative Operations

• Paper Insights #35 - Microsecond Consensus for Microsecond Applications
• Affiliation: VMWare Research
• Learning Goals: Consensus, Hardware RDMA

• Paper Insights #36 - Orca: A Distributed Serving System for Transformer-Based Generative Models
• Affiliation: Seoul National University
• Learning Goals: GPU, Model Serving, Distributed Machine Learning
  
  
• Paper Insights #37 - The Honey Badger of BFT Protocols
• Affiliation: University of Illinois, Tsinghua University, Cornell University
• Learning Goals: Blockchain, Byzantine Faults, Consensus

• Paper Insights #38 - Availability in Globally Distributed Storage Systems
• Affiliation: Google
• Leaning Goals: Availability