1. Outline Distributed Mutual Exclusion Introduction Performance measures
Centralized algorithm
Non-token-based algorithms
Lamport’s algorithm
Ricart-Agrawala algorithm
Token-based algorithms
Suzuki-Kasami’s broadcast algorithm
Singhal’s heuristic algorithm
Raymond’s tree-based algorithm
Comparison
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2. Announcement
Next Tuesday (Feb. 18, 2003) Dr. Ted Baker will lecture on distributed deadlock detection (Chapter 7)
Please read the materials ahead of time
Please review the local deadlock detection algorithms (Chapter 3)
I will post his lecture notes on the class web too
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3. The Critical Section Problem
When processes (centralized or distributed) interact through shared resources, the integrity of the resources may be violated if the accesses are not coordinated
The resources do not record all the changes
A process may obtain inconsistent values
The final state of the shared resource may be inconsistent
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4. An Example
Suppose we have two processes, (one is called producer and the other one consumer), the shared variable counter is 5 initially
Producer: counter = counter +1
P1: load counter, r1
P2: add r1, #1, r2
P3: store r2, counter
Consumer: counter = counter - 1
C1: load counter, r1
C2: add r1, #-1, r2
C3: store r2, counter
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5. An Example - cont. A particular execution sequence
P1: load counter, r1
P2: add r1, #1, r2
--- Context switch ----
C1: load counter, r1
C2: add r1, #-1, r2
C3: store r2, counter
P3: store r2, counter
What is the value of counter?
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6. An Example - cont. A particular execution sequence
C1: load counter, r1
C2: add r1, #-1, r2
--- Context switch ----
P1: load counter, r1
P2: add r1, #1, r2
P3: store r2, counter
C3: store r2, counter
What is the value of counter this time?
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7. An Example - cont. A particular execution sequence
C1: load counter, r1
C2: add r1, #-1, r2
C3: store r2, counter
--- Context switch ----
P1: load counter, r1
P2: add r1, #1, r2
P3: store r2, counter
What is the value of counter this time?
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8. Mutual Exclusion
One solution to the problem is that at any time at most only one process can access the shared resources
This solution is known as mutual exclusion
A critical section is a code segment in a process which shared resources are accessed
A process can have more than one critical section
There are problems which involve shared resources where mutual exclusion is not the optimal solution
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9. The Structure of Processes
Structure of process Pi
repeat
entry section
critical section
exit section
reminder section
until false;
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10. Mutual Exclusion in Traditional OS
Through semaphores, monitors or other programming-language constructs
Semaphore S – integer variable
can only be accessed via two indivisible (atomic) operations
wait (S): while S 0 do no-op; S := S – 1;
signal (S): S := S + 1;
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11. Mutual Exclusion in Traditional OS – cont.
Shared variables
var mutex : semaphore
initially mutex = 1
Process Pi
repeat
wait(mutex);
critical section
signal(mutex);
remainder section
until false;
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12. Distributed Mutual Exclusion
Due to the absence of shared memory, solutions for mutual exclusion based on shared memory cannot be used in distributed systems
It must be based on message passing
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13. Performance Measure for Distributed Mutual Exclusion
The number of messages per CS invocation
Synchronization delay
The time required after a site leaves the CS and before the next site enters the CS
System throughput 1/(sd+E), where sd is the synchronization delay and E the average CS execution time
Response time
The time interval a request waits for its CS execution to be over after its request messages have been sent out
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14. Performance Measure for Distributed Mutual Exclusion
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15. A Centralized Algorithm
It is a simple solution
One site, called the control site, is responsible for granting permission to the CS execution
To request the CS, a site sends a REQUEST message to the control site
When a site is done with CS execution, it sends a RELEASE message to the control site
The control site queues up the requests for the CS and grant them permission
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16. A Centralized Algorithm – cont.
Process 1 asks the coordinator for permission to enter a critical region. Permission is granted
Process 2 then asks permission to enter the same critical region. The coordinator does not reply.
When process 1 exits the critical region, it tells the coordinator, when then replies to 2
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17. A Centralized Algorithm – cont.
Analysis of the centralized algorithm
There is a single point of failure
Number of messages per CS
The synchronization delay
System throughput
Response time
Best case
Worst case
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18. Distributed Solutions
Non-token-based algorithms
Use timestamps to order requests and resolve conflicts between simultaneous requests
Lamport’s algorithm and Ricart-Agrawala Algorithm
Token-based algorithms
A unique token is shared among the sites
A site is allowed to enter the CS if it possess the token and continues to hold the token until its CS execution is over; then it passes the token to the next site
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19. Lamport’s Distributed Mutual Exclusion Algorithm
This algorithm is based on the total ordering using Lamport’s clocks
Each process keeps a Lamport’s logical clock
Each process is associated with a unique id that can be used to break the ties
In the algorithm, each process keeps a queue, request_queuei, which contains mutual exclusion requests ordered by their timestamp and associated id
Ri of each process consists of all the processes
The communication channel is assumed to be FIFO
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20. Lamport’s Distributed Mutual Exclusion Algorithm – cont.
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21. Lamport’s Distributed Mutual Exclusion Algorithm – cont.
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22. Lamport’s Distributed Mutual Exclusion Algorithm – cont.
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23. Lamport’s Distributed Mutual Exclusion Algorithm – cont.
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24. Lamport’s Distributed Mutual Exclusion Algorithm – cont.
Performance analysis
Number of messages per CS
Synchronization delay
Throughput
Response time
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25. Ricart-Agrawala Algorithm
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26. Ricart-Agrawala Algorithm – cont.
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27. Ricart-Agrawala Algorithm – cont.
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28. Ricart-Agrawala Algorithm – cont.
Performance analysis
Number of messages per CS
Synchronization delay
Throughput
Response time
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29. A Simple Toke Ring Algorithm
When the ring is initialized, one process is given the token
The token circulates around the ring
It is passed from k to k+1 (modulo the ring size)
When a process acquires the token from its neighbor, it checks to see if it is waiting to enter its critical section
If so, it enters its CS
When exiting from its CS, it passes the token to the next
Otherwise, it passes the token to the next
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30. A Simple Toke Ring Algorithm – cont.
An unordered group of processes on a network.
A logical ring constructed in software.
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31. A Simple Toke Ring Algorithm – cont.
Performance analysis
Number of messages per CS
Synchronization delay
Response time
Problems
Lost token
Process crash
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32. Suzuki-Kasami’s Algorithm
Data structures
Each site maintains a vector consisting the largest sequence number received so far from other sites
The token consists of a queue of requesting sites and an array of integers, consisting of the sequence number of the request that a site executed most recently
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33. Suzuki-Kasami’s Algorithm – cont.
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34. Suzuki-Kasami’s Algorithm – cont.
Performance analysis
Number of messages per CS invocation
Synchronization delay
Response time
System throughput
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35. Singhal’s Heuristic Algorithm
In this algorithm, each site maintains information about the state of other sites in the system
The site only sends the request to a subset of sites that most likely have the token will in a near future
This reduces the number of messages per CS execution
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36. Raymond’s Tree-Based Algorithm
In this algorithm, sites are logically arranged as a directed tree
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37. Comparison of Distributed Mutual Exclusion Algorithms
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38. Summary
Mutual exclusion is one solution to the critical section problem
Due to the absence of shared memory, distributed mutual exclusion algorithms are message-based
Centralized algorithms
Non-token-based algorithms
Token-based algorithms
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