Search

Given a distributed computation and a global predicate, predicate detection involves determining whether there exists at least one consistent cut (or global state) of the computation that satisfies the predicate. On the other hand, computation slicing is concerned with computing the smallest subcomputation (with the least number of consistent cuts) that contains all consistent cuts of the computation satisfying the predicate. In this paper, we investigate the relationship between predicate detection and computation slicing and show that the two problems are actually equivalent. Specifically, given an algorithm to detect a predicate b in a computation C, we derive an algorithm to compute the slice of C with respect to b. The time complexity of the (derived) slicing algorithm is O(n∣E∣T), where n is the number of processes, E is the set of events, and O(T) is the time complexity of the detection algorithm. We discuss how the "equivalence" result of this paper can be utilized to derive a faster algorithm for solving the genera/predicate detection problem in many cases. Slicing algorithms described in our earlier papers are all offline in nature. In this paper, we also present two online algorithms for computing the slice. The first algorithm can be used to compute the slice for a general predicate. Its amortized time complexity is O(n(c+n)T) per event, where c is the average concurrency in the computation and O(T) is the time complexity of the detection algorithm. The second algorithm can be used to compute the slice for a regular predicate. Its amortized time complexity is only O(n2) per event. [ABSTRACT FROM AUTHOR]

The group mutual exclusion problem extends the traditional mutual exclusion problem by associating a type (or a group) 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. We present a distributed algorithm for solving the group mutual exclusion problem based on the notion of surrogate-quorum. 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 quorum-based algorithms for group mutual exclusion, our algorithm achieves a low message complexity of O(q) and a low (amortized) bit-message complexity of O(bqr), where q is the maximum size of a quorum, b is the maximum number of processes from which a node can receive critical section requests, and r is the maximum size of a request while maintaining both synchronization delay and waiting time at two message hops. As opposed to some existing quorum-based algorithms, our algorithm can adapt without performance penalties to dynamic changes in the set of groups. Our simulation results indicate that our algorithm outperforms the existing quorum-based algorithms for group mutual exclusion by as much as 45 percent in some cases. We also discuss how our algorithm can be extended to satisfy certain desirable properties such as concurrent entry and unnecessary blocking freedom. [ABSTRACT FROM AUTHOR]

We present a novel key predistribution scheme that uses deployment knowledge to divide deployment regions into overlapping clusters, each of which has its own distinct key space. Through careful construction of these clusters, network resilience is improved, without compromising connectivity or communications overhead. Experimental results show significant improvement in performance over existing schemes based on deployment knowledge. [ABSTRACT FROM AUTHOR]