Distributed algorithm

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

DONAR: decentralized server selection for cloud services

Abstract: Geo-replicated services need an effective way to direct client requests to a particular location, based on performance, load, and cost. This paper presents DONAR, a distributed system that can offload the burden of replica selection, while providing these services with a sufficiently expressive interface for specifying mapping policies. Most existing approaches for replica selection rely on either central coordination (which has reliability, security, and scalability limitations) or distributed heuristics (which lead to suboptimal request distributions, or even instability). In contrast, the distributed mapping nodes in DONAR run a simple, efficient algorithm to coordinate their replica-selection decisions for clients. The protocol solves an optimization problem that jointly considers both client performance and server load, allowing us to show that the distributed algorithm is stable and effective. Experiments with our DONAR prototype--providing replica selection for CoralCDN and the Measurement Lab--demonstrate that our algorithm performs well "in the wild." Our prototype supports DNS- and HTTP-based redirection, IP anycast, and a secure update protocol, and can handle many customer services with diverse policy objectives.

Distance Based Thresholds for Cluster Head Selection in Wireless Sensor Networks

Abstract: Central to the cluster-based routing protocols is the cluster head (CH) selection procedure that allows even distribution of energy consumption among the sensors, and therefore prolonging the lifespan of a sensor network We propose a distributed CH selection algorithm that takes into account the distances from sensors to a base station that optimally balances the energy consumption among the sensors NS-2 simulations show that our proposed scheme outperforms existing algorithms in terms of the average node lifespan and the time to first node death

Proof labeling schemes

Abstract: This paper addresses the problem of locally verifying global properties. Several natural questions are studied, such as “how expensive is local verification?” and more specifically, “how expensive is local verification compared to computation?” A suitable model is introduced in which these questions are studied in terms of the number of bits a vertex needs to communicate. The model includes the definition of a proof labeling scheme (a pair of algorithms- one to assign the labels, and one to use them to verify that the global property holds). In addition, approaches are presented for the efficient construction of schemes, and upper and lower bounds are established on the bit complexity of schemes for multiple basic problems. The paper also studies the role and cost of unique identities in terms of impossibility and complexity, in the context of proof labeling schemes. Previous studies on related questions deal with distributed algorithms that simultaneously compute a configuration and verify that this configuration has a certain desired property. It turns out that this combined approach enables the verification to be less costly sometimes, since the configuration is typically generated so as to be easily verifiable. In contrast, our approach separates the configuration design from the verification. That is, it first generates the desired configuration without bothering with the need to verify it, and then handles the task of constructing a suitable verification scheme. Our approach thus allows for a more modular design of algorithms, and has the potential to aid in verifying properties even when the original design of the structures for maintaining them was done without verification in mind.

Resource discovery in distributed networks

Abstract: In large distributed networks of computers, it is often the case that a subset of machines wants to cooperate to perform a task. Before they can do so, these machines need to learn of the existence of each other. In this paper we are interested in distributed algorithms whereby machines in a network learn of other machines in the network by making queries to machines they already know. The algorithms should be efficient both in terms of the time required and in terms of the total network communication required until all machines have discovered all other machines. We propose a very simple algorithm called Name-Dropper whereby all machines learn about each other within O(log’ n) rounds with high probability, where n is the number of machines in the network. The total number of connections required is O(n log2 n) and the total number of pointers which must be communicated is O(n2 log2 n), with high probability. Each of the preceding bounds is optimal to within polylogarithmic factors.

Computing separable functions via gossip

Abstract: Motivated by applications to sensor, peer-to-peer, and ad-hoc networks, we study the problem of computing functions of values at the nodes in a network in a totally distributed manner. In particular, we consider separable functions, which can be written as linear combinations of functions of individual variables. Known iterative algorithms for averaging can be used to compute the normalized values of such functions, but these algorithms do not extend in general to the computation of the actual values of separable functions.The main contribution of this paper is the design of a distributed randomized algorithm for computing separable functions based on properties of exponential random variables. We bound the running time of our algorithm in terms of the running time of an information spreading algorithm used as a subroutine by the algorithm. Since we are interested in totally distributed algorithms, we consider a randomized gossip mechanism for information spreading as the subroutine. Combining these algorithms yields a complete and simple distributed algorithm for computing separable functions.The second contribution of this paper is an analysis of the information spreading time of the gossip algorithm. This analysis yields an upper bound on the information spreading time, and therefore a corresponding upper bound on the running time of the algorithm for computing separable functions, in terms of the conductance of an appropriate stochastic matrix. These bounds imply that, for a class of graphs with small spectral gap (such as grid graphs), the time used by our algorithm to compute averages is of a smaller order than the time required for the computation of averages by a known iterative gossip scheme [5].