Paper Reading

Paper reading notes on distributed systems, storage, and cluster management, ordered by publication date.

  1. MIT6.824-MapReduce

    The third year of university has been quite intense, leaving me with little time to continue my studies on 6.824, so my progress stalled at Lab 1. With a bit more free time during …

  2. MIT6.824 GFS

    This article introduces the Google File System (GFS) paper published in 2003, which proposed a distributed file system designed to store large volumes of data reliably, meeting …

  3. MIT6.824 Bigtable

    I recently found a translated version of the Bigtable paper online and saved it, but hadn’t gotten around to reading it. Lately, I’ve noticed that Bigtable shares many …

  4. DFS-Haystack

    The primary project in my group is a distributed file system (DFS) that provides POSIX file system semantics. The approach to handle “lots of small files” (LOSF) is …

  5. MIT6.824-Raft

    Finally, I managed to complete Lab 02 during this winter break, which had been on hold for quite some time. I was stuck on one of the cases in Test 2B for a while. During the …

  6. MIT6.824-RaftKV

    Earlier, I looked at the code of Casbin-Mesh because I wanted to try GSOC. Casbin-Mesh is a distributed Casbin application based on Raft. This RaftKV in MIT6.824 is quite similar, …

  7. MIT6.824-ZooKeeper

    This article mainly discusses the design and practical considerations of the ZooKeeper system, such as wait-free and lock mechanisms, consistency choices, system-provided APIs, and …

  8. MIT6.824 Chain Replication

    This post provides a brief overview of the Chain Replication (CR) paper, which introduces a simple but effective algorithm for providing linearizable consistency in storage …

  9. MIT6.824 AuroraDB

    This article introduces the design considerations of AWS’s database product, Aurora, including storage-compute separation, single-writer multi-reader architecture, and …

  10. Dynamo: Amazon’s Highly Available Key-value Store

    An old paper by AWS, Dynamo has been in the market for a long time, and the architecture has likely evolved since the paper’s publication. Despite this, the paper was …

  11. Percolator: Large-scale Incremental Processing Using Distributed Transactions and Notifications

    It has been a while since I last studied, and I wanted to learn something interesting. This time, I’ll be covering Percolator, a distributed transaction system. I won’t …

  12. Borg: Large-scale Cluster Management at Google with Borg

    Study how Borg combines admission control, scheduling, and resource sharing to operate large clusters efficiently.