Showing papers by "Aravind Srinivasan published in 2007"

Cross-layer latency minimization in wireless networks with SINR constraints

Abstract: Recently, there has been substantial interest in the design of cross-layer protocols for wireless networks. These protocols optimize certain performance metric(s) of interest (e.g. latency, energy, rate) by jointly optimizing the performance of multiple layers of the protocol stack. Algorithm designers often use geometric-graph-theoretic models for radio interference to design such cross-layer protocols. In this paper we study the problem of designing cross-layer protocols for multi-hop wireless networks using a more realistic Signal to Interference plus Noise Ratio (SINR) model for radio interference. The following cross-layer latency minimization problem is studied: Given a set V of transceivers, and a set of source-destination pairs, (i) choose power levels for all the transceivers, (ii) choose routes for all connections, and (iii) construct an end-to-end schedule such that the SINR constraints are satisfied at each time step so as to minimize the make-span of the schedule (the time by which all packets have reached their respective destinations). We present a polynomial-time algorithm with provable worst-case performance guarantee for this cross-layer latency minimization problem. As corollaries of the algorithmic technique we show that a number of variants of the cross-layer latency minimization problem can also be approximated efficiently in polynomial time. Our work extends the results of Kumar et al. (Proc. SODA, 2004) and Moscibroda et al. (Proc. MOBIHOC, 2006). Although our algorithm considers multiple layers of the protocol stack, it can naturally be viewed as compositions of tasks specific to each layer --- this allows us to improve the overall performance while preserving the modularity of the layered structure.

Cost-Sharing Mechanisms for Network Design

Abstract: We consider a single-source network design problem from a game-theoretic perspective. Gupta, Kumar and Roughgarden (Proc. 35th Annual ACM STOC, pp. 365–372, 2003) developed a simple method for a single-source rent-or-buy problem that also yields the best-known approximation ratio for the problem. We show how to use a variant of this method to develop an approximately budget-balanced and group strategyproof cost-sharing method for the problem. The novelty of our approach stems from our obtaining the cost-sharing methods for the rent-or-buy problem by carefully combining cost-shares for the simpler Steiner tree problem. Our algorithm is conceptually simpler than the previous such cost-sharing method due to Pal and Tardos (Proc. 44th Annual FOCS, pp. 584–593, 2003), and improves the previously-known approximation factor of 15 to 4.6.

Approximation algorithms for stochastic and risk-averse optimization

Abstract: We present improved approximation algorithms in stochastic optimization. We prove that the multi-stage stochastic versions of covering integer programs (such as set cover and vertex cover) admit essentially the same approximation algorithms as their standard (non-stochastic) counterparts; this improves upon work of Swamy & Shmoys that shows an approximability which depends multiplicatively on the number of stages. We also present approximation algorithms for facility location and some of its variants in the 2-stage recourse model, improving on previous approximation guarantees.

Efficient lookup on unstructured topologies

Abstract: We present LMS, a protocol for efficient lookup on unstructured networks. Our protocol uses a virtual namespace without imposing specific topologies. It is more efficient than existing lookup protocols for unstructured networks, and thus is an attractive alternative for applications in which the topology cannot be structured as a Distributed Hash Table (DHT). We present analytic bounds for the worst-case performance of LMS. Through detailed simulations (with up to 100,000 nodes), we show that the actual performance on realistic topologies is significantly better. We also show in both simulations and a complete implementation (which includes over five hundred nodes) that our protocol is inherently robust against multiple node failures and can adapt its replication strategy to optimize searches according to a specific heuristic. Moreover, the simulation demonstrates the resilience of LMS to high node turnover rates, and that it can easily adapt to orders of magnitude changes in network size. The overhead incurred by LMS is small, and its performance approaches that of DHTs on networks of similar size

Distributed ranked search

Abstract: P2P deployments are a natural infrastructure for building distributed search networks. Proposed systems support locating and retrieving all results, but lack the information necessary to rank them. Users, however, are primarily interested in the most relevant results, not necessarily all possible results. Using random sampling, we extend a class of well-known information retrieval ranking algorithms such that they can be applied in this decentralized setting. We analyze the overhead of our approach, and quantify how our system scales with increasing number of documents, system size, document to node mapping (uniform versus non-uniform), and types of queries (rare versus popular terms). Our analysis and simulations show that a) these extensions are efficient, and scale with little overhead to large systems, and b) the accuracy of the results obtained using distributed ranking is comparable to that of a centralized implementation.

Randomized algorithms and probabilistic analysis in wireless networking

Abstract: Devices connected wirelessly, in various forms including computers, hand-held devices, ad hoc networks, and embedded systems, are expected to become ubiquitous all around us. Wireless networks pose interesting new challenges, some of which do not arise in standard (wired) networks. This survey discusses some key probabilistic notions - both randomized algorithms and probabilistic analysis - in wireless networking.