Fall 2020: CS 553 (Call no 39828) Distributed Computing Systems


Instructor: Ajay Kshemkalyani
Email: ajay@uic.edu
Class meeting times: TR 2:00-3:15pm (Tuesday and Thursday)
Room: on-line synchronous in Blackboard Collaborate Ultra
Office Hours in Blackboard Collaborate Ultra, immediately after class: TR 3:15 - 3:30pm and beyond as needed

Course Description

  1. Models: synchronous/asynchronous; shared memory/message-passing
  2. Global states and snapshots; time models and clock synchronization
  3. Distributed graph algorithms
  4. Group communication - total order and causal order
  5. Reasoning with knowledge
  6. Distributed shared memory - coherence, models, register constructions, atomic snapshots (applications to multicore architectures)
  7. Checkpointing, rollback recovery; distributed debugging
  8. Agreement and consensus (with malicious and non-malicious process behavior)
  9. Failure detectors
  10. Self-stabilizing systems
  11. Peer-to-peer systems, e.g., Chord, Tapestry, Content-Addressible Network, BitTorrent
  12. other current topics, e.g., sensor networks
See detailed table of contents of the textbook below by going to the link at Cambridge University Press or Amazon. This course focuses on distributed algorithms and theoretical principles. By the end of the course, you will be able to appreciate that these algorithms have wide applications in practical distributed systems.

Resources

  1. Textbook: Distributed Computing: Principles, Algorithms, and Systems, by Kshemkalyani and Singhal, Cambridge University Press, March 2011 edition
    South Asian reprint edition, Dec 2010, ISBN-10: 1107648904, ISBN-13: 978-1107648906
  2. Course notes are here
  3. An Overview Chart
  4. Winners of the Dijkstra Award for Most Influential Paper in Distributed Computing, 2000-.
  5. Selected papers and other material from the literature will be posted on the web or distributed in class.
  6. Suggested Topics and Papers for class presentation (tentative list; to be updated)
    1. Self-Stabilization:
      E.W. Dijkstra, Self-stabilizing systems in spite of distributed control, Communications of the ACM, vol. 17, no. 11, pp. 643-644, Nov. 1974.
      plus Chapter 17 (Self-stabilization)
    2. Paxos and Raft:
      L. Lamport, Paxos Made Simple, ACM SIGACT News (Distributed Computing Column) 32, 4 (121, December 2001) 51-58.
      W. Bolosky et al, Paxos replicated state machines as the basis of a high-performance data store, NSDI 2011
      D. Ongaro and J. Ousterhout, In Search of an Understandable Consensus Algorithm (Extended Version).
    3. C. Flanagan, S. Freund, FastTrack: efficient and precise dynamic race detection, PLDI 2009.
    4. W Cai, F He, X Lv, Y Cheng, A transparent selective undo algorithm for collaborative editing, IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2017.
    5. Transactional Memory - software and hardware
    6. Conflict-free Replicated Data Types (CRDTs)
    7. Hadoop and Mapreduce:
      J. Dean and S. Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, OSDI'04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December, 2004.
      plus Hadoop
    8. Concurrent Data Structures
    9. Blockchain and bitcoin

Course Structure and Class Participation

The course format will be in two parts.
  1. For the first part, the instructor will teach.
    Attendance when the instructor is teaching is not compulsory, but you must attend all the tests/exams. However, if you miss class, it is your responsibility to find out what was announced and what was covered, from other students.
  2. The second part involves active student participation and is planned as follows. Each presentation will be made by a team of 1 or 2 students, depending on the final enrollment which will be known only in the third week of class.
    The class presentations will be on an assortment of topics of current interest. Each group chooses a paper/topic from a list of topics provided around the 5th week of class. This is only a starting point. Once you select a topic from the list (to be provided), you may have to identify more basic or fundamental papers on that specific topic for presentation. Pick the most basic/ fundamantal papers that are rich in new ideas. They must also have algorithmic content.
    Attendance when the student presentations are going on is compulsory.
There is also a programming project requirement (in a language of your choice).

Prerequisites

Algorithm analysis and design (cs401) is suggested (but not required); or permission of the instructor.

Grading

The following is only a tentative breakup of the evaluation scheme and will be finalized after the second week of class, depending on the final enrollment in the course. The course grade is on the curve, i.e., this is relative grading - how you perform with respect to the others in the class.

Tentative course progress chart (will be updated as we progress)