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Ranganath Atreya

dblp:41/852 · DBLP profile ↗
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5ranked-venue papers
4as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 4 · 3 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%
Theoretical computer science
1 paper
Distributed computing theory · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › mutual exclusion
group mutual exclusion
0.112007
A Quorum-Based Group Mutual Exclusion Algorithm for a Distributed System with Dynamic Group Set · IEEE Trans. Parallel Distributed Syst. 2007
Distributed systems
mutual exclusion
0.112007
A Quorum-Based Group Mutual Exclusion Algorithm for a Distributed System with Dynamic Group Set · IEEE Trans. Parallel Distributed Syst. 2007
Distributed systems › quorum systems
quorum protocols
0.112007
A Quorum-Based Group Mutual Exclusion Algorithm for a Distributed System with Dynamic Group Set · IEEE Trans. Parallel Distributed Syst. 2007
Distributed computing theory › distributed complexity
message complexity
0.012007
A Quorum-Based Group Mutual Exclusion Algorithm for a Distributed System with Dynamic Group Set · IEEE Trans. Parallel Distributed Syst. 2007

Methods — techniques the papers use, named apart from their topics

simulation · 0.1quorum systems · 0.1quorum system · 0.1
YearPublicationVenuePosition
2007 Efficient detection of a locally stable predicate in a distributed system
Ranganath Atreya, Neeraj Mittal, Ajay D. Kshemkalyani, Vijay K. Garg, Mukesh Singhal
J. Parallel Distributed Comput.1
2007 A Quorum-Based Group Mutual Exclusion Algorithm for a Distributed System with Dynamic Group Set
abstract
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.
Ranganath Atreya, Neeraj Mittal, Sathya Peri
IEEE Trans. Parallel Distributed Syst.1
2005 A Dynamic Group Mutual Exclusion Algorithm Using Surrogate-Quorums
abstract
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 surrogate-quorum. Intuitively, the 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, the 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 quorum-based algorithms, the algorithm has high maximum concurrency of n, where n is the number of processes in the system. The existing quorum-based algorithms assume that the number of groups is static and does not change during runtime. However, the algorithm can adapt without performance penalties to dynamic changes in the number of groups. Simulation results indicate that our algorithm outperforms the existing quorum-based algorithms for group mutual exclusion by as much as 50% in some cases
Ranganath Atreya, Neeraj Mittal
ICDCS1
2004 Finding Satisfying Global States: All for One and One for All
abstract
Summary form only given. 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 sub-computation - with the least number of consistent cuts - that contains all consistent cuts of the computation satisfying the predicate. We investigate the relationship between predicate detection and computation slicing and show that the two problems are 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|) times the time-complexity of the detection algorithm, where n is the number of processes and E is the set of events. We discuss how the "equivalence " result can be utilized to derive a faster algorithm for solving the general predicate detection problem. Slicing algorithms described in our earlier papers are all off-line in nature. We also give an online algorithm for computing the slice for a predicate that can be detected efficiently. The amortized time-complexity of the algorithm is O(n(c + n)) times the time-complexity of the detection algorithm, where c is the average concurrency in the computation.
Neeraj Mittal, Alper Sen 0001, Vijay K. Garg, Ranganath Atreya
IPDPS4
2003 Detecting Locally Stable Predicates Without Modifying Application Messages
Ranganath Atreya, Neeraj Mittal, Vijay K. Garg
OPODIS1