Mutual-Exclusion Algorithm.ppt
Mutual-Exclusion Algorithm.ppt
Mutual exclusion is a fundamental concept in computer science and concurrent programming that ensures that only one process or thread can access a shared resource at a time. It prevents multiple processes from simultaneously executing a critical section of code, which can lead to race conditions and data inconsistencies. Various algorithms have been developed to achieve mutual exclusion, and in this essay, we will explore some of the key concepts and techniques used in designing mutual exclusion algorithms. To begin, let's define some basic terms. A critical section is a portion of code that accesses shared resources, such as variables or data structures. The goal of mutual exclusion is to ensure that only one process can execute its critical section at any given time, while other processes are blocked or waiting for access. One of the simplest approaches to mutual exclusion is the concept of locks. A lock is a synchronization primitive that can be acquired by a process to gain exclusive access to a resource. When a process wants to enter its critical section, it attempts to acquire the lock. If the lock is currently held by another process, the requesting process is blocked until the lock becomes available. Once a process finishes executing its critical section, it releases the lock, allowing another process to acquire it. A widely used lock-based mutual exclusion algorithm is the Peterson's algorithm. It was proposed by Gary L. Peterson in 1981 and works for two processes. Peterson's algorithm relies on two shared variables: turn and flag. The turn variable indicates whose turn it is to enter the critical section, while the flag variable signals a process's intention to enter the critical section. While Peterson's algorithm provides mutual exclusion for two processes, it suffers from some limitations. Firstly, it relies on busy-waiting, where a process continuously checks for the turn of the other process. Busy-waiting wastes CPU cycles and is not suitable for systems where processes need to wait for long durations. Secondly, Peterson's algorithm is susceptible to memory coherence issues on modern multi-core processors. To overcome these limitations, more sophisticated mutual exclusion algorithms have been developed. One such algorithm is Dekker's algorithm, proposed by Th. J. Dekker in 1965. Dekker's algorithm extends Peterson's algorithm to provide mutual exclusion for two processes without busy-waiting. It introduces two additional shared variables, flag0 and flag1, to avoid busy-waiting. Dekker's algorithm avoids busy-waiting by introducing the loop in step 5, which allows a process to yield the CPU and avoid wasting resources while waiting for its turn. However, Dekker's algorithm suffers from the problem of mutual exclusion violation when both processes attempt to enter their critical sections simultaneously.
Recomendados
Mais conteúdo relacionado
Similar a Mutual-Exclusion Algorithm.ppt
Último
Mutual-Exclusion Algorithm.ppt
- 1. Page 1 Page 1 Mutual Exclusion* Distributed Systems Prepared By: Er. Shikha Manrai
- 2. Page 2 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
- 3. Page 3 Centralized Systems Mutual exclusion via: – Test & set – Semaphores – Messages – Monitors
- 4. Page 4 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
- 5. Page 5 Distributed Mutual Exclusion • Centralized Algorithm • Token Ring Algorithm • Distributed Algorithm • Decentralized Algorithm
- 6. Page 6 Centralized algorithm • Mimic single processor system • One process elected as coordinator P C request(R) grant(R) 1. Request resource 2. Wait for response 3. Receive grant 4. access resource 5. Release resource release(R)
- 7. Page 7 Centralized algorithm If another process claimed resource: – Coordinator does not reply until release – Maintain queue • Service requests in FIFO order P0 C request(R) grant(R) release(R) P1 P2 request(R) Queue P1 request(R) P2 grant(R)
- 8. Page 8 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
- 9. Page 9 Token Ring algorithm Assume known group of processes – Some ordering can be imposed on group – Construct logical ring in software – Process communicates with neighbor P0 P1 P2 P3 P4 P5
- 10. Page 10 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 P0 P1 P2 P3 P4 P5 token(R)
- 11. Page 11 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
- 12. Page 12 Ricart & Agrawala algorithm • Distributed algorithm using reliable multicast and logical clocks • Process wants to enter critical section: – Compose message containing: • Identifier (machine ID, process ID) • Name of resource • Timestamp (totally-ordered Lamport) – Send request to all processes in group – Wait until everyone gives permission – Enter critical section / use resource
- 13. Page 13 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
- 14. Page 14 Ricart & Agrawala algorithm • N points of failure • A lot of messaging traffic • Demonstrates that a fully distributed algorithm is possible
- 15. Page 15 Lamport’s Mutual Exclusion Each process maintains request queue – Contains mutual exclusion requests Requesting critical section: – Process Pi sends request(i, Ti) to all nodes – Places request on its own queue – When a process Pj receives a request, it returns a timestamped ack Lamport time
- 16. Page 16 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
- 17. Page 17 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
- 18. Characteristics of Decentralized Algorithms No machine has complete information about the system state Machines make decisions based only on local information Failure of one machine does not ruin the algorithm Three is no implicit assumption that a global clock exists
- 19. Page 19 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
- 20. Page 20 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
- 21. Page 21 The Decentralized Algorithm • Every replica has a coordinator that controls access to it (the coordinator is the node that stores it) • For a process to use the resource it must receive permission from m > n/2 coordinators • This guarantees exclusive access as long as a coordinator only grants access to one process at a time
- 22. Page 22 The Decentralized Algorithm • The coordinator notifies the requester when it has been denied access as well as when it is granted – Requester must “count the votes”, and decide whether or not overall permission has been granted or denied • If a process (requester) gets fewer than m votes it will wait for a random time and then ask again
- 23. Page 23 Analysis • If a resource is in high demand, multiple requests will be generated • It’s possible that processes will wait a long time to get permission • Deadlock? • Resource usage drops
- 24. Page 24 Analysis • More robust than the central coordinator approach and the distributed approaches. If one coordinator goes down others are available. – If a coordinator fails and resets then it will not remember having granted access to one requestor, and may then give access to another. According to the authors, it is highly unlikely that this will lead to a violation of mutual exclusion. (See the text for a probabilistic argument.)