Reading list for our distributed systems class

-
Here is our reading list. It follows our distributed systems course schedule provided in the previous post. I tried to choose the papers that are foundational, comprehensive, and readable by a first year graduate student. In some cases, I omitted very long or hard to follow papers ---even though they may be foundational papers--- and instead included some follow up papers that summarized the concept better.

I assign these papers as review material for the corresponding topic. The students then choose one from the batch and review that one critically. But I hope that the students can read all of them if possible.

These papers should be all available with a Google Scholar search. Some of these papers appear in open access venues. If that is not the case, often the authors make their papers available freely at their websites, as they have the right to do. "Authors retain the right to use the accepted author manuscript for personal use, internal institutional use and for permitted scholarly posting provided that these are not for purposes of commercial use or systematic distribution".

Consensus and Paxos

  • Paxos Made Moderately Complex. Robbert Van Renesse and Deniz Altinbuken, ACM Computing Surveys, 2015.
  • Paxos made live - An engineering perspective. Tushar D. Chandra, Robert Griesemer, Joshua Redstone. ACM PODC, Pages: 398 - 407, 2007 
  • ZooKeeper: Wait-free coordination for internet-scale systems P. Hunt, M. Konar, F. P. Junqueira, and B. Reed  USENIX ATC 2010. 
  • The Chubby Lock Service for Loosely-Coupled Distributed Systems. Mike Burrows, OSDI 2006.
  • In Search of an Understandable Consensus Algorithm. Diego Ongaro, John Ousterhout, USENIX ATC, 2014.
  • WPaxos: Wide Area Network Flexible Consensus. Ailidani Ailijiang, Aleksey Charapko, Murat Demirbas, Tevfik Kosar, IEEE TPDS, 2019.
  • Dissecting the Performance of Strongly-Consistent Replication Protocols. Ailidani Ailijiang, Aleksey Charapko, Murat Demirbas, Sigmod 2019.
  • Viewstamped replication revisited. Barbara Liskov and James Cowling. MIT-CSAIL-TR-2012-021, 2012.
  • Chain Replication for Supporting High Throughput and Availability. Robbert van Renesse and Fred B. Schneider, OSDI 2004. 
  • FAWN: A Fast Array of Wimpy Nodes David G. Andersen and Jason Franklin and Michael Kaminsky and Amar Phanishayee and Lawrence Tan and Vijay Vasudevan. SOSP 2009.
  • CORFU: A shared log design for Flash clusters. Mahesh Balakrishnan, Dahlia Malkhi, Vijayan Prabhakaran, Ted Wobber, Michael Wei, John D. Davis, NSDI'2012.

Failure detectors and fault-tolerance

  • Unreliable Failure Detectors for Reliable Distributed Systems, Tushar Deepak Chandra and Sam Toueg, Journal of the ACM, 1996.
  • Simple Testing Can Prevent Most Critical Failures: An Analysis of Production Failures in Distributed Data-Intensive Systems. Ding Yuan, Yu Luo, Xin Zhuang, Guilherme Renna Rodrigues, Xu Zhao, Yongle Zhang, Pranay U. Jain, and Michael Stumm, OSDI 2014.
  • Why Does the Cloud Stop Computing? Lessons from Hundreds of Service Outages,  Haryadi S. Gunawi, Mingzhe Hao, and Riza O. Sumintom Agung Laksono, Anang D. Satria, Jeffry Adityatama, and Kurnia J. Eliazar, SOCC 2016.
  • Does The Cloud Need Stabilizing? Murat Demirbas, Aleksey Charapko, Ailidani Ailijiang, 2018.
  • TaxDC: A Taxonomy of nondeterministic concurrency bugs in datacenter distributed systems, Tanakorn Leesatapornwongsa, Jeffrey F. Lukman, Shan Lu, Haryadi S. Gunawi, ASPLOS 2016.

Time and snapshots

  • Time, Clocks, and the Ordering of Events in a Distributed System. Leslie Lamport, Commn. of the ACM,  1978.
  • Logical Physical Clocks and Consistent Snapshots in Globally Distributed Databases. Sandeep Kulkarni, Murat Demirbas, Deepak Madeppa, Bharadwaj Avva, and Marcelo Leone, 2014. https://cse.buffalo.edu/tech-reports/2014-04.pdf
  • Distributed Snapshots: Determining Global States of a Distributed System. K. Mani Chandy Leslie Lamport, ACM Transactions on Computer Systems, 1985.

Cloud computing

  • Tail at scale. Jeff Dean, Luiz Andre Barroso, Commn of the ACM, 2013. 
  • Lessons from Giant-Scale Services. Eric A. Brewer, IEEE Internet Computing, 2001.
  • Above the Clouds: A Berkeley View of Cloud Computing. Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy H. Katz, Andrew Konwinski, Gunho Lee, David A. Patterson, Ariel Rabkin, Ion Stoica and Matei Zaharia.  EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2009-28
February 10, 2009.
  • Serverless computing: One step forward, two steps back, UC Berkeley, CIDR 2019.
  • Cloud Programming Simplified: A Berkeley View on Serverless Computing, 2019. https://arxiv.org/abs/1902.03383
  • On designing and deploying Internet scale services, James Hamilton, LISA 2007.

NoSQL and distributed databases

  • Life beyond Distributed Transactions: an Apostate’s Opinion. Pat Helland, CIDR 2007. 
  • Optimistic Replication. Yasushi Saito and Marc Shapiro, ACM Computing Surveys, 2005.
  • CAP Twelve Years Later: How the "Rules" Have Changed. Eric Brewer, IEEE Computer, 2012
  • PNUTS: Yahoo!'s Hosted Data Serving Platform. Brian F. Cooper, Raghu Ramakrishnan, Utkarsh Srivastava, Adam Silberstein, Philip Bohannon, Hans-Arno Jacobsen, Nick Puz, Daniel Weaver and Ramana Yerneni, VLDB 2008.
  • Dynamo: Amazon’s Highly Available Key-Value Store. Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall and Werner Vogels, ACM SIGOPS 2007.
  • Bigtable: A Distributed Storage System for Structured Data. Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber, ACM Transactions on Computer Systems, 2008.
  • Spanner: Google’s Globally-Distributed Database James C. Corbett, Jeffrey Dean, Michael Epstein, Andrew Fikes, Christopher Frost, JJ Furman,Sanjay Ghemawat, Andrey Gubarev, Christopher Heiser, Peter Hochschild, Wilson Hsieh,Sebastian Kanthak, Eugene Kogan, Hongyi Li, Alexander Lloyd, Sergey Melnik, David Mwaura,David Nagle, Sean Quinlan, Rajesh Rao, Lindsay Rolig, Yasushi Saito, Michal Szymaniak,Christopher Taylor, Ruth Wang, Dale Woodford, ACM Trans on Computer Systems, 2013.

Big data processing

  • MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay Ghemawat, Commn of the ACM, 2008.
  • Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, Ion Stoica. NSDI 2012. April 2012.
  • TUX2: Distributed Graph Computation for Machine Learning, Wencong Xiao,  Jilong Xue, Youshan Miao, Zhen Li, Cheng Chen and Ming Wu, Wei Li, Lidong Zhou, NSDI 2017.
  • Proteus: agile ML elasticity through tiered reliability in dynamic resource markets. Aaron Harlap, Alexey Tumanov, Andrew Chung, Gregory R. Ganger, Phillip B. Gibbons. EuroSys, 2017.
  • TensorFlow: A system for large-scale machine learning, Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. OSDI 2016.

Decentralized ledgers

  • Practical Byzantine Fault Tolerance, Miguel Castro and Barbara Liskov, OSDI'99.
  • Bitcoin: A Peer-to-Peer Electronic Cash System, Satoshi Nakamoto, 2008.
  • Scalable and Probabilistic Leaderless BFT Consensus through Metastability, Team Rocket, Maofan Yin, Kevin Sekniqi, Robbert van Renesse, and Emin Gun Sirer, 2019.
  • Untangling Blockchain: A Data Processing View of Blockchain Systems. Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, IEEE Transactions on Knowledge and Data Engineering, 2017.
  • Bridging Paxos and Blockchain Consensus. A. Charapko, A. Ailijiang, M. Demirbas,  IEEE Blockchain, 2018. http://www.cse.buffalo.edu/~demirbas/publications/bridging.pdf
  • Blockchains from a distributed computing perspective, Maurice Herlihy, Commn of the ACM, 2017.

Comments

Post a Comment

Popular posts from this blog

Hints for Distributed Systems Design

-
This is with apologies to Butler Lampson, who published the " Hints for computer system design " paper 40 years ago in SOSP'83. I don't claim to match that work of course. I just thought I could draft this post to organize my thinking about designing distributed systems and get feedback from others. I start with the same  disclaimer Lampson gave. These hints are not novel, not foolproof recipes, not laws of design, not precisely formulated, and not always appropriate. They are just hints.  They are context dependent, and some of them may be controversial. That being said, I have seen these hints successfully applied in distributed systems design throughout my 25 years in the field, starting from the theory of distributed systems (98-01), immersing into the practice of wireless sensor networks (01-11), and working on cloud computing systems both in the academia and industry ever since. These heuristic principles have been applied knowingly or unknowingly and has proven
Read more >>

Learning about distributed systems: where to start?

-
This is definitely not a "learn distributed systems in 21 days" post. I recommend a principled, from the foundations-up, studying of distributed systems, which will take a good three months in the first pass, and many more months to build competence after that. If you are practical and coding oriented you may not like my advice much. You may object saying, "Shouldn't I learn distributed systems with coding and hands on? Why can I not get started by deploying a Hadoop cluster, or studying the Raft code." I think that is the wrong way to go about learning distributed systems, because seeing similar code and programming language constructs will make you think this is familiar territory, and will give you a false sense of security. But, nothing can be further from the truth. Distributed systems need radically different software than centralized systems do.  --A. Tannenbaum This quotation is literally the first sentence in my distributed systems syllabus. Inst
Read more >>

Making database systems usable

-
Image
C. J. Date's Sigmod 1983 keynote, "Database Usability", was prescient. Usability is the most important thing to the customers. They care less about impressive benchmarks or clever algorithms, and more about whether they can operate and use a database efficiently to query, update, analyze, and persist their data with minimal headache. (BTW, does anyone have a link to the contents of this Sigmod'83 talk? There is no transcript around, except for this short abstract .) The paper we cover today is from Sigmod 2007. It takes on the database usability problem raised in that 1983 keynote head-on, and calls out that the king is still naked.  Let's give some context for the year 2007. Yes, XML format was still popular then. The use-case in the paper is XQuery. The paper does not contain any  reference to json. MongoDB would be released in 2009 with the document model; and that seems to be great timing for some of the usability pains mentioned in the paper! Web 2.0 was in
Read more >>

Looming Liability Machines (LLMs)

-
As part of our zoom reading group ( wow, 4.5 years old now ), we discussed a paper that uses LLMs for automatic root cause analysis (RCA) for cloud incidents. This was a pretty straightforward application of LLMs. The proposed system employs an LLM to match incoming incidents to incident handlers based on their alert types, predicts the incident's root cause category, and provides an explanatory narrative. The only customization is through prompt-engineering. Since this is a custom domain, I think a more principled and custom-designed  machine learning system would be more appropriate rather than adopting LLMs. Anyways, the use of LLMs for RCAs spooked me vicerally. I couldn't find the exact words during the paper discussion, but I can articulate this better now. Let me explain. RCA is serious business Root cause analysis (RCA) is the process of identifying the underlying causes of a problem/incident, rather than just addressing its symptoms. One RCA heuristic is asking 5 Why&#
Read more >>

Foundational distributed systems papers

-
I talked about the importance of reading foundational papers last week. To followup, here is my compilation of foundational papers in the distributed systems area. (I focused on the core distributed systems area, and did not cover networking, security, distributed ledgers, verification work etc. I even left out distributed transactions, I hope to cover them at a later date.)  I classified the papers by subject, and listed them in chronological order. I also listed expository papers and blog posts at the end of each section. Time and State in Distributed Systems Time, Clocks, and the Ordering of Events in a Distributed System. Leslie Lamport, Commn. of the ACM,  1978. Distributed Snapshots: Determining Global States of a Distributed System. K. Mani Chandy Leslie Lamport, ACM Transactions on Computer Systems, 1985. Virtual Time and Global States of Distributed Systems.  Mattern, F. 1988. Practical uses of synchronized clocks in distributed systems. B. Liskov, 1991. Expository papers
Read more >>

Advice to the young

-
Image
I notice I haven't written any advice posts recently. Here is a collection of my advice posts pre 2020. I've been feeling all this elderly wisdom pent up in me, ready to pour at any moment. So here it goes. Get ready to quench your thirst from my fount of wisdom. No man, think for yourself, only get what works for you. It is called foundations, not theory Foundations of computer science (or rather any field of study) are the most important topics you can learn. These lay down the frame of thinking/perspective for that area of study. Yet, I am saddened to hear these called as "theory", and labeled as "unpractical". This couldn't be farther from the truth. Take a look at how I recommend studying distributed systems . Don't you dare call this "theory" and "unpractical". This lays the bedrock that you build your practice on. Don't skimp on the foundations. Don't build your home on quicksand. Keep your hands dirty, your mind cl
Read more >>

Linearizability: A Correctness Condition for Concurrent Objects

-
Image
This paper is from Herlihy and Wing appeared in ACM Transactions on Programming Languages and Systems 1990. This is the canonical reference for the linearizability definition. I had not read this paper in detail before, so I thought it would be good to go to the source to see if there are additional delightful surprises in the original text. Hence, this post. I will dive into a technical analysis of the paper first, and then discuss some of my takes toward the end. I had written an accessible explanation of linearizability earlier; you may want to read that first. I will assume an understanding of linearizability to keep this review at reasonable length. Introduction I love how the old papers just barge in with the model, without bothered by pleasantries such as motivation of the problem. These are the first two sentences of the introduction. "A concurrent system consists of a collection of sequential processes that communicate through shared typed objects . This model encompass
Read more >>

Scalable OLTP in the Cloud: What’s the BIG DEAL?

-
Image
This paper is from Pat Helland, the apostate philosopher of database systems, overall a superb person, and a good friend of mine. The paper appeared this week at CIDR'24. (Check out the program for other interesting papers). The motivating question behind this work is: " What are the asymptotic limits to scale for cloud OLTP (OnLine Transaction Processing) systems? " Pat says that the CIDR 2023 paper "Is Scalable OLTP in the Cloud a Solved Problem?" prompted this question.  The answer to the question? Pat says that the answer lies in the joint responsibility of database and the application. If you know of Pat's work, which I have summarized several in this blog , you would know that Pat has been advocating along these lines before. But this paper provides a very crisp, specific, concrete answer. Read on for my summary of the paper. Disclaimer: This is a wisdom and technical information/detail packed 13-page paper, so I will try my best to summarize the sa
Read more >>

Understanding the Performance Implications of Storage-Disaggregated Databases

-
Image
Storage-compute disaggregation in databases has emerged as a pivotal architecture in cloud environments, as evidenced by Amazon ( Aurora ), Microsoft ( Socrates ), Google (AlloyDB), Alibaba ( PolarDB ), and Huawei (Taurus). This approach decouples compute from storage, allowing for independent and elastic scaling of compute and storage resources. It provides fault-tolerance at the storage level. You can then share the storage for other services, such as adding read-only replicas for the databases. You can even use the storage level for easier sharding of your database. Finally, you can also use this for exporting a changelog asynchronously to feed into peripheral cloud services, such as analytics. Disaggregated architecture was the topic of Sigmod 23 panel . I think this quote summarizes the industry's thinking on the topic. "Disaggregated architecture is here, and is not going anywhere. In a disaggregated architecture, storage is fungible, and computing scales independently.
Read more >>

Designing Data Intensive Applications (DDIA) Book

-
Image
We started reading this book as part of Alex Petrov's book club . We just got started, so you can join us, by joining the discord channel above. We meet Wednesday's 11am Eastern Time.  Previously we had read transaction processing book by Grey and Reuters. This page links to my summaries of that book. Chp 1. Reliable, Scalable, and Maintainable Applications I love the diagrams opening each chapter. Beautiful! The first chapter consists of warm up stuff. It talks about the definitions of reliabilty, scalability, and maintainability. It is still engaging, because  it is written with an educator and technical blogger voice, rather than a dry academic voice. This book came out on 2017. Martin is working on the new version. So if you have comments for things to focus on for the new version, it would be helpful to collect them in a document and email it to Martin. For example, I am curious about how the below paragraph from the Preface will get revised with 8 more years of hindsight:
Read more >>