1. Distributed Systems and Algorithms
Sukumar Ghosh
University of Iowa
Fall 2009

2. What is a distributed system?5 2 1 4 8 3 7 10 6 9
A network of processes. The nodes are processes, and the edges are communication channels.

3. Fact
Processor population is exploding. Technology has dramatically reduced the cost of processors, and user demands for services have increased the scale of systems.

4. Examples
Large networks are very commonplace these days. Think of the world wide web. Other examples are:
- Social Networks
Sensor networks
BitTorrent for downloading video / audio
Skype for making free audio and video communication
Process control networks
Computational grids (OSG, Teragrid,
Network of mobile robots

5. Sensor Network
Checking the structural integrity

6. Mobile robots I-Swarm Robot (See a YouTube video
on the I-Swarm Robots)

7. Goal P Q
Typically, the application layers of the processes interact with one another to meet a common objective, or achieve individual objectives using
shared resources.
Copy X from P to Q
(P&Q may be two processes 10,000 miles apart)

8. Goal continued
Distributed computing relies on inter-process communication, which involves other layers of networking too (like TCP implementing a reliable end-to-end communication channel, or the Media Access protocol used in Ethernet LAN or Wireless networks).
What are the distributed computing issues in
establishing inter-process communication? P Q
Create a reliable
channel from P to Q

9. Why distributed systems
Geographic distribution of processes
Resource sharing (example: P2P networks, grids)
Computation speed up (as in a grid)
Fault tolerance

10. Important services Internet auction Web search Google earth
Internet banking
Web search
Net meeting
Distance education
Video distribution
Internet auction
Google earth
Skype
Publish subscribe

11. Important issues Clocks are not synchronized No shared address space
Knowledge is local
Clocks are not synchronized
No shared address space
Topology and routing : everything is dynamic
Scalability: all solutions do not scale well
Processes and links fail: Fault tolerance

12. Some common subproblems
Leader election
Mutual exclusion
Time synchronization
Distributed snapshot
Replica management
Consensus

13. Implementation Practical distributed systems must have a real
network as its backbone. However, such systems
can be simulated on a shared-memory
multiprocessor, or even on a single processor.
(How will you do it?)

14. Models
We will reason about distributed systems using models. There are many dimensions of variability in distributed systems. Examples:
types of processors
interprocess communication mechanisms,
timing assumptions
failure classes,
security features, etc

15. Models
Models are simple abstractions that help overcome the variability -- abstractions that preserve the essential features, but hide the implementation details from observers who view the system at a higher level.
We don’t care whether a process runs on a PC or a Mac.
A link may use optical communication or radio communication
For simplicity, in a specific model, we may assume that all clocks are perfectly synchronized.

16. A classification Client-server model Server is the coordinator
Peer-to-peer model
No unique coordinator

17. Parallel vs Distributed
In both parallel and distributed systems, the
events are partially ordered.
In parallel systems, the primarily issue is speed-up
In distributed systems the primary issues
are fault-tolerance, availability, and scalability

18. Objective of the course
To understand how a system works, why it
fails, how can we guarantee our design, so
that it behaves the way we want it to behave.
A system that “sometimes works” is no good.
We are not discussing implementation or programming,
although they are also important issues.