Distributed Systems Classics (2017)
First reported by Nvartolomei ·
If you need to understand the foundational concepts of distributed systems, a curated reading list is now readily available.
Nicolae Vartolomei compiled a list of influential papers in distributed systems, originally in 2017 and updated in 2022, intended as a starting point for understanding the field. The list includes foundational research from 1978 to 2014. Key papers cover topics such as time and event ordering in distributed systems (Lamport, 1978), the Byzantine Generals Problem (Lamport, Shostak, Pease, 1982), distributed snapshots for global states (Chandy & Lamport, 1985), and the impossibility of distributed consensus with faulty processes (Fischer, Lynch, Paterson, 1985). It also features work on highly-available replicated systems (Oki & Liskov, 1988), the Part-time Parliament consensus algorithm (Lamport, 1998), Paxos Made Simple (Lamport, 2001), Bitcoin's peer-to-peer electronic cash system (Nakamoto, 2008), Conflict-free replicated data types (Shapiro et al., 2011), and an understandable consensus algorithm (Ongaro & Ousterhout, 2014).
This compilation highlights the enduring relevance of foundational papers in distributed systems, many of which were written decades ago. The inclusion of works on consensus algorithms, fault tolerance, and data replication underscores the persistent challenges in building reliable distributed architectures. The list serves as a testament to how early theoretical work continues to influence modern systems, from cloud infrastructure to blockchain technologies.
The selection emphasizes that core problems in distributed systems have not fundamentally changed, but rather our approaches to solving them have evolved. Papers on Byzantine fault tolerance and consensus, for instance, are directly relevant to the security and reliability of decentralized systems like cryptocurrencies and verifiable computation platforms. This resource is invaluable for anyone building or researching systems where coordination and consistency across multiple nodes are paramount.
AI-written summary. May contain errors.