Mutual Exclusion Algorithms: Centralized, Token Ring, Ricart-Agrawala, Lamport, Slides of Operating Systems

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Distributed Systems

Mutual Exclusion

Mutual Exclusion?

• A condition in which there is a set of processes, only one of which is

able to access a given resource or perform a given function at any

time

Distributed Mutual Exclusion

• Assume there is agreement on how a resource is identified
  • Pass identifier with requests
• Create an algorithm to allow a process to obtain exclusive access to a

resource

Distributed Mutual Exclusion

• Centralized Algorithm
• Token Ring Algorithm
• Distributed Algorithm
• Decentralized Algorithm

Centralized algorithm

If another process claimed resource:

• Coordinator does not reply until release
• Maintain queue
  • Service requests in FIFO order P

C request(R) grant(R) release(R) P

P

request(R)

Queue

P

request(R) P

grant(R)

Centralized algorithm

Benefits

• Fair
  • All requests processed in order
• Easy to implement, understand, verify

Problems

• Process cannot distinguish being blocked from a dead coordinator
• Centralized server can be a bottleneck

Token Ring algorithm

• Initialization
  • Process 0 gets token for resource R
• Token circulates around ring
  • From Pi to P(i+1)mod N
• When process acquires token
  • Checks to see if it needs to enter critical section
  • If no, send token to neighbor
  • If yes, access resource
    • Hold token until done P 0 P 1 P 2 P 3 P 4 P 5 token(R)

Token Ring algorithm

• Only one process at a time has token
  • Mutual exclusion guaranteed
• Order well-defined
  • Starvation cannot occur
• If token is lost (e.g. process died)
  • It will have to be regenerated
• Does not guarantee FIFO order

Ricart & Agrawala algorithm

• When process receives request:
  • If receiver not interested:
    • Send OK to sender
  • If receiver is in critical section
    • Do not reply; add request to queue
  • If receiver just sent a request as well:
    • Compare timestamps: received & sent messages
    • Earliest wins
    • If receiver is loser, send OK
    • If receiver is winner, do not reply, queue
• When done with critical section
  • Send OK to all queued requests

Ricart & Agrawala algorithm

• N points of failure
• A lot of messaging traffic
• Demonstrates that a fully distributed algorithm is possible

Lamport’s Mutual Exclusion

Entering critical section (accessing resource):

• Pi received a message ( ack or release ) from every other process with a timestamp larger than Ti
• Pi’s request has the earliest timestamp in its queue

Difference from Ricart-Agrawala:

• Everyone responds … always - no hold-back
• Process decides to go based on whether its request is the earliest in its queue

Lamport’s Mutual Exclusion

Releasing critical section:

• Remove request from its own queue
• Send a timestamped release message
• When a process receives a release message
  • Removes request for that process from its queue
  • This may cause its own entry have the earliest timestamp in the queue, enabling it to access the critical section

Decentralized Algorithm

• Based on the Distributed Hash Table (DHT) system structure

previously introduced

• Peer-to-peer
• Object names are hashed to find the successor node that will store them
• Here, we assume that n replicas of each object are stored

Placing the Replicas

• The resource is known by a unique name: rname
  • Replicas: rname-0, rname-I, …, rname-(n-1)
  • rname-i is stored at succ(rname-i) , where names and site names are hashed as before
  • If a process knows the name of the resource it wishes to access, it also can generate the hash keys that are used to locate all the replicas