In this paper we present a game theoretic framework for obtaining a user-optimal load balancing scheme in heterogeneous distributed systems. We formulate the static load balancing problem in heterogeneous distributed systems as a noncooperative game among users. For the proposed noncooperative load balancing game, we present the structure of the Nash equilibrium. Based on this structure we deri...

This paper is concerned with distributed limited memory prediction for continuous-time linear stochastic systems with multiple sensors. A distributed fusion with the weighted sum structure is applied to the optimal local limited memory predictors. The distributed prediction algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. The algorith...

In this paper, we describe a new distributed computing framework for crystal structure prediction that is capable of performing crystal structure searches for flexible molecules within any space group and with an arbitrary number of molecules in the asymmetric unit. The distributed computing framework includes a series of tightly integrated computer programs for generating the molecule's force ...

EEcient data mining algorithms are crucial for eeective knowledge discovery. We present the Multi-Stream Dependency Detection (msdd) data mining algorithm that performs a systematic search for structure in multivariate time series of categorical data. The systematicity of msdd's search makes implementation of both parallel and distributed versions straightforward. Distributing the search for st...

In this paper we propose a new access structure, called GHT*, based on generalized hyperplane tree (GHT) and distributed dynamic hashing (DDH) techniques. GHT* is a distributed structure which allows to perform range search in a metric space according to a distance function d. The structure does not require a central directory and it is able to gracefully scale through splits of one bucket at a...

Distributed pseudorandom functions (DPRFs) originally introduced by Naor, Pinkas and Reingold (EUROCRYPT ’99) are pseudorandom functions (PRFs), whose computation is distributed to multiple servers. Although by distributing the function computation, we avoid single points of failures, this distribution usually implies the need for multiple interactions with the parties (servers) involved in the...