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26
Sharedmemory mutual exclusion: Major research trends since
 Distributed Computing
, 1986
"... * Exclusion: At most one process executes its critical section at any time. ..."
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Cited by 47 (6 self)
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* Exclusion: At most one process executes its critical section at any time.
Mutual Exclusion in Asynchronous Systems with Failure Detectors
 J. Parallel Distrib. Comput
, 2002
"... This paper defines the faulttolerant mutual exclusion problem in a messagepassing asynchronous system and determines the weakest failure detector to solve the problem. This failure detector, which we call the trusting failure detector, and which we denote by , is strictly weaker than the pe ..."
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Cited by 16 (3 self)
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This paper defines the faulttolerant mutual exclusion problem in a messagepassing asynchronous system and determines the weakest failure detector to solve the problem. This failure detector, which we call the trusting failure detector, and which we denote by , is strictly weaker than the perfect failure detector but strictly stronger than the eventually perfect failure detector #P. The paper shows that a majority of correct processes is necessary to solve the problem with .Moreover,T is also the weakest failure detector to solve the faulttolerant group mutual exclusion problem.
A Dynamic Group Mutual Exclusion Algorithm using SurrogateQuorums
 in Proceedings of the IEEE International Conference on Distributed Computing Systems (ICDCS
, 2005
"... The group mutual exclusion problem extends the traditional mutual exclusion problem by associating a type with each critical section. In this problem, processes requesting critical sections of the same type can execute their critical sections concurrently. However, processes requesting critical sect ..."
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Cited by 12 (4 self)
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The group mutual exclusion problem extends the traditional mutual exclusion problem by associating a type with each critical section. In this problem, processes requesting critical sections of the same type can execute their critical sections concurrently. However, processes requesting critical sections of different types must execute their critical sections in a mutually exclusive manner. In this paper, we provide a distributed algorithm for solving the group mutual exclusion problem based on the notion of surrogatequorum. Intuitively, our algorithm uses the quorum that has been successfully locked by a request as a surrogate to service other compatible requests for the same type of critical section. Unlike the existing quorumbased algorithms for group mutual exclusion, our algorithm achieves a low message complexity of O(q), where q is the maximum size of a quorum, while maintaining both synchronization delay and waiting time at two message hops. Moreover, like the existing quorumbased algorithms, our algorithm has high maximum concurrency of n, where n is the number of processes in the system. The existing quorumbased algorithms assume that the number of groups is static and does not change during runtime. However, our algorithm can adapt without performance penalties to dynamic changes in the number of groups. Simulation results indicate that our algorithm outperforms the existing quorumbased algorithms for group mutual exclusion by as much as 50 % in some cases. 1.
Group Mutual Exclusion in Tree Networks
 Journal of Information Science and Engineering
, 2003
"... The group mutual exclusion (GME) problem deals with sharing a set of (m) mutually exclusive resources among all (n) processes of a network. Processes are allowed to be in a critical section simultaneously provided they request the same resource. We present three group mutual exclusion solutions for ..."
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Cited by 8 (0 self)
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The group mutual exclusion (GME) problem deals with sharing a set of (m) mutually exclusive resources among all (n) processes of a network. Processes are allowed to be in a critical section simultaneously provided they request the same resource. We present three group mutual exclusion solutions for tree networks. All three solutions do not use process identifiers and use bounded size messages. They achieve the best contextswitch complexity, which is O(min(n, m)). The first solution uses a fixed root of the tree and uses 0 to O(n) messages per critical section entry. This solution supports an unbounded degree of concurrency, thus providing the maximum resource utilization. The second solution also uses a fixed root, but uses a reduced number of messages for the critical section entry. It generates an average of O(log n) messages per critical section entry and also allows an unbounded degree of concurrency. We remove the restriction of using a fixed root in the third solution in addition to maintaining all other desirable properties of the second solution.
ABSTRACT An Ω(n log n) Lower Bound on the Cost of Mutual Exclusion
"... We prove an Ω(n log n) lower bound on the number of nonbusywaiting memory accesses by any deterministic algorithm solving n process mutual exclusion that communicates via shared registers. The cost of the algorithm is measured in the state change cost model, a variation of the cache coherent model. ..."
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Cited by 7 (1 self)
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We prove an Ω(n log n) lower bound on the number of nonbusywaiting memory accesses by any deterministic algorithm solving n process mutual exclusion that communicates via shared registers. The cost of the algorithm is measured in the state change cost model, a variation of the cache coherent model. Our bound is tight in this model. We introduce a novel information theoretic proof technique. We first establish a lower bound on the information needed by processes to solve mutual exclusion. Then we relate the amount of information processes can acquire through shared memory accesses to the cost they incur. We believe our proof technique is flexible and intuitive, and may be applied to a variety of other problems and system models.
Group Mutual Exclusion in Token Rings
 In SIROCCO 2001, The 8th International Colloquium On Structural Information and Communication Complexity Proceedings
, 2001
"... The group mutual exclusion (GME) problem was introduced by Joung [6]. The GME solution allows n processes to share m mutually exclusive resources. We first present a group mutual exclusion algorithm (Algorithm GME ) for anonymous token rings. The space requirement and the size of messages of thi ..."
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Cited by 3 (1 self)
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The group mutual exclusion (GME) problem was introduced by Joung [6]. The GME solution allows n processes to share m mutually exclusive resources. We first present a group mutual exclusion algorithm (Algorithm GME ) for anonymous token rings. The space requirement and the size of messages of this algorithm depend only on the number of shared resources (O(logm) bits). So, the proposed algorithm solves the problem suggested in [7], which is to obtain a solution using messages of bounded size. All costs related to the time depend on n. We then present two variations of Algorithm GM E . We design the second algorithm (Algorithm mGME) such that its cost depends mainly on the m instead of n. The third algorithm (Algorithm nmGME ) is a general algorithm which takes advantage of the lowest value between n and m. Keywords Distributed algorithms, group mutual exclusion, mutual exclusion. 1
A DelayOptimal Group Mutual Exclusion Algorithm for a Tree Network
, 2006
"... The group mutual exclusion problem is an extension of the traditional mutual exclusion problem in which every critical section is associated with a type or a group. Processes requesting critical sections of the same type can execute their critical sections concurrently. However, processes requesting ..."
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Cited by 2 (1 self)
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The group mutual exclusion problem is an extension of the traditional mutual exclusion problem in which every critical section is associated with a type or a group. Processes requesting critical sections of the same type can execute their critical sections concurrently. However, processes requesting critical sections of different types must execute their critical sections in a mutually exclusive manner. We present an efficient distributed algorithm for solving the group mutual exclusion problem when processes are arranged in the form of a tree. Our algorithm is derived from Beauquier et al.’s group mutual exclusion algorithm for a tree network. The message complexity of our algorithm is at most 3hmax, wherehmax is the maximum height of the tree when rooted at any process. Its waiting time and synchronization delay, measured in terms of number of message hops, are at most 2hmax and hmax, respectively. Our algorithm has optimal synchronization delay for the class of tree network based algorithms for group mutual exclusion in which messages are only exchanged over the edges in the tree. Our simulation experiments indicate that our algorithm outperforms Beauquier et al.’s group mutual exclusion algorithm by as much as 70 % in some cases. Key words: distributed system, resource management, group mutual exclusion, tree network, optimal synchronization delay 1
A General Resource Allocation Synchronization Problem
 In Proceedings of the 21st International Conference on Distributed Computing Systems (ICDCS21
, 2001
"... We introduce a new synchronization problem called GRASP. We show that this problem is very general, in that it can provide solutions with strong properties to a wide range of previouslystudied and new problems. The primary goals of this work are to unify and clarify the relationships between existi ..."
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Cited by 2 (0 self)
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We introduce a new synchronization problem called GRASP. We show that this problem is very general, in that it can provide solutions with strong properties to a wide range of previouslystudied and new problems. The primary goals of this work are to unify and clarify the relationships between existing and new synchronization problems, to provide fast answers about what solutions are possible to new problems (or stronger versions of existing ones), and to provide a baseline against which to compare optimized, problemspecic solutions. We present a sharedmemory solution to this problem. Our solution is based on a new solution to the Dining Philosophers problem with constant failure locality (this implies that a nonfaulty process can be caused to wait indenitely only by the failure of a process within a constant number of steps of it in the graph). We use the powerful tool of waitfree transactions to simplify our solution without restricting concurrency. Email: keane@danet.com. ...
A Time Complexity Lower Bound for Adaptive Mutual Exclusion ∗
, 2007
"... We consider the time complexity of adaptive mutual exclusion algorithms, where “time ” is measured by counting the number of remote memory references required per criticalsection access. For systems that support (only) read, write, and comparison primitives (such as compareandswap), we establish ..."
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Cited by 1 (0 self)
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We consider the time complexity of adaptive mutual exclusion algorithms, where “time ” is measured by counting the number of remote memory references required per criticalsection access. For systems that support (only) read, write, and comparison primitives (such as compareandswap), we establish a lower bound that precludes a deterministic algorithm with o(k) time complexity, where k is point contention. In particular, it is impossible to construct a deterministic O(log k) algorithm based on such primitives.