Distributed algorithm

About: Distributed algorithm is a research topic. Over the lifetime, 20416 publications have been published within this topic receiving 548109 citations.

Joint Beamforming and Power Splitting for MISO Interference Channel With SWIPT: An SOCP Relaxation and Decentralized Algorithm

Abstract: This paper considers a power splitting-based MISO interference channel for simultaneous wireless information and power transfer (SWIPT), where each single antenna receiver splits the received signal into two streams of different power for decoding information and harvesting energy separately. We aim to minimize the total transmission power by joint beamforming and power splitting (JBPS) under both the signal-to-interference-plus-noise ratio (SINR) constraints and energy harvesting (EH) constraints. The JBPS problem is nonconvex and has not yet been well addressed in the literature. Moreover, decentralized algorithm design for JBPS based on local channel state information (CSI) and limited information exchange remains open. In this paper, we first propose a novel relaxation method named second-order cone programming (SOCP) relaxation to address the JBPS problem. We formulate the relaxed problem as an SOCP and present two sufficient conditions under which the SOCP relaxation is tight. For the case when the SOCP solution is not necessarily optimal to the JBPS problem, a closed-form feasible-solution-recovery method is provided. Then, we develop a distributed algorithm for the JBPS problem based on primal-decomposition (PD) method. The PD-based distributed algorithm consists of a master problem and a set of subproblems. The former is solved by using subgradient method while the latter are solved using coordinate descent method. Finally, numerical results validates the efficiency of the proposed algorithms.

Asynchronous distributed averaging on communication networks

Abstract: Distributed algorithms for averaging have attracted interest in the control and sensing literature. However, previous works have not addressed some practical concerns that will arise in actual implementations on packet-switched communication networks such as the Internet. In this paper, we present several implementable algorithms that are robust to asynchronism and dynamic topology changes. The algorithms are completely distributed and do not require any global coordination. In addition, they can be proven to converge under very general asynchronous timing assumptions. Our results are verified by both simulation and experiments on Planetlab, a real-world TCP/IP network. We also present some extensions that are likely to be useful in applications.

Nash Equilibrium Computation in Subnetwork Zero-Sum Games With Switching Communications

Abstract: In this paper, we investigate a distributed Nash equilibrium computation problem for a time-varying multi-agent network consisting of two subnetworks, where the two subnetworks share the same objective function. We first propose a subgradient-based distributed algorithm with heterogeneous stepsizes to compute a Nash equilibrium of a zero-sum game. We then prove that the proposed algorithm can achieve a Nash equilibrium under uniformly jointly strongly connected (UJSC) weight-balanced digraphs with homogenous stepsizes. Moreover, we demonstrate that for weighted-unbalanced graphs a Nash equilibrium may not be achieved with homogenous stepsizes unless certain conditions on the objective function hold. We show that there always exist heterogeneous stepsizes for the proposed algorithm to guarantee that a Nash equilibrium can be achieved for UJSC digraphs. Finally, in two standard weight-unbalanced cases, we verify the convergence to a Nash equilibrium by adaptively updating the stepsizes along with the arc weights in the proposed algorithm.

Asynchronous implementation of failure detectors

Abstract: Unreliable failure detectors introduced by Chandra and Toueg are abstract mechanisms that provide information on process failures. On the one hand, failure detectors allow to state the minimal requirements on process failures that allow to solve problems that cannot be solved in purely asynchronous systems. But, on the other hand, they cannot be implemented in such systems: their implementation requires that the underlying distributed system be enriched with additional assumptions. The usual failure detector implementations rely on additional synchrony assumptions (e.g., partial synchrony). This paper proposes a new look at the implementation of failure detectors and more specifically at Chandra-Toueg’s failure detectors. The proposed approach does not rely on synchrony assumptions (e.g., it allows the communication delays to always increase). It is based on a query-response mechanism and assumes that the query/response messages exchanged obey a pattern where the responses from some processes to a query arrive among the (n − f ) first ones (n being the total number of processes, f the maximum number of them that can crash, with 1 ≤ f< n). When we consider the particular case f =1 , and the implementation of a failure detector of the class denoted S (the weakest class that allows to solve the consensus problem), the additional assumption the underlying system has to satisfy boils down to a simple channel property, namely, there is eventually a pair of processes (pi ,p j) such that the channel connecting them is never the slowest among the channels connecting pi or pj to the other processes. A probabilistic analysis shows that this requirement is practically met in asynchronous distributed systems.

Global optimization using local information with applications to flow control

Abstract: Flow control in high speed networks requires distributed routers to make fast decisions based only on local information in allocating bandwidth to connections. While most previous work on this problem focuses on achieving local objective functions, in many cases it may be necessary to achieve global objectives such as maximizing the total flow. This problem illustrates one of the basic aspects of distributed computing: achieving global objectives using local information. Papadimitriou and Yannakakis (1993) initiated the study of such problems in a framework of solving positive linear programs by distributed agents. We take their model further, by allowing the distributed agents to acquire more information over time. We therefore turn attention to the tradeoff between the running time and the quality of the solution to the linear program. We give a distributed algorithm that obtains a (1+/spl epsiv/) approximation to the global optimum solution and runs in a polylogarithmic number of distributed rounds. While comparable in running time, our results exhibit a significant improvement on the logarithmic ratio previously obtained by Awerbuch and Azar (1994). Our algorithm, which draws from techniques developed by Luby and Nisan (1993) is considerably simpler than previous approximation algorithms for positive linear programs, and thus may have practical value in both centralized and distributed settings.