A
Aravind Srinivasan
Researcher at University of Maryland, College Park
Publications - 278
Citations - 14614
Aravind Srinivasan is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Approximation algorithm & Wireless network. The author has an hindex of 60, co-authored 266 publications receiving 13711 citations. Previous affiliations of Aravind Srinivasan include Graz University of Technology & Bell Labs.
Papers
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Proceedings Article
Approximation algorithms for stochastic clustering
TL;DR: In this paper, the authors consider stochastic settings for clustering, and develop provably good (approximation) algorithms for a number of these notions, which allow one to obtain better approximation ratios compared to the usual deterministic clustering setting.
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Approximation algorithms for throughput maximization in wireless networks with delay constraints
TL;DR: This work develops algorithms to compute end-to-end delay constraints with provable performance guarantees for arbitrary instances, with general interference models, and gives a stable flow vector with a total throughput within a factor of O of the maximum.
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Retrieval scheduling for collaborative multimedia presentations
TL;DR: An application-layer broker (ALB) that employs a content-based, client-centric approach to negotiate with the servers and to identify the best server for the requested objects to maximize client buffer utilization.
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Approximation algorithms for throughput maximization in wireless networks with delay constraints
TL;DR: This paper develops algorithms to compute end-to-end delay constraints for multi-hop wireless networks with provable performance guarantees for arbitrary instances, with general interference models, and gives a stable flow vector with a total throughput within a factor of O of the maximum.
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A Unified Approach to Online Matching with Conflict-Aware Constraints
Pan Xu,Yexuan Shi,Hao Cheng,John P. Dickerson,Karthik Abinav Sankararaman,Aravind Srinivasan,Yongxin Tong,Leonidas Tsepenekas +7 more
TL;DR: An efficient linear programming (LP) based online algorithm is proposed and it is proved theoretically that it has nearly-optimal online performance and two LP-based heuristics experimentally dominate the baseline algorithms, aligning with the theoretical predictions and supporting the unified approach.