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Workflow Scheduling Algorithms for Grid Computing
"... Workflow scheduling is one of the key issues in the management of workflow execution. Scheduling is a process that maps and manages execution of interdependent tasks on distributed resources. It introduces allocating suitable resources to workflow tasks so that the execution can be completed to sat ..."
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Cited by 45 (6 self)
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Workflow scheduling is one of the key issues in the management of workflow execution. Scheduling is a process that maps and manages execution of interdependent tasks on distributed resources. It introduces allocating suitable resources to workflow tasks so that the execution can be completed to satisfy objective functions specified by users. Proper scheduling can have significant impact on the performance of the system. In this chapter, we investigate existing workflow scheduling algorithms developed and deployed by various Grid projects.
Rapidly Mixing Markov Chains with Applications in Computer Science and Physics
, 2006
"... Monte Carlo algorithms often depend on Markov chains to sample from very large data sets. A key ingredient in the design of an efficient Markov chain is determining rigorous bounds on how quickly the chain “mixes,” or converges, to its stationary distribution. This survey provides an overview of sev ..."
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Cited by 28 (0 self)
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Monte Carlo algorithms often depend on Markov chains to sample from very large data sets. A key ingredient in the design of an efficient Markov chain is determining rigorous bounds on how quickly the chain “mixes,” or converges, to its stationary distribution. This survey provides an overview of several useful techniques.
Design of an Optimal Loosely Coupled Heterogeneous Multiprocessor System
 Proc. ED&TC
, 1996
"... This paper presents an approach for mapping tasks optimal to hardware and software components in order to design a realtime system. The tasks are derived from an algorithm and are represented by a taskgraph. The performance of the algorithm on the resulting realtime system will meet the specified ..."
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Cited by 26 (0 self)
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This paper presents an approach for mapping tasks optimal to hardware and software components in order to design a realtime system. The tasks are derived from an algorithm and are represented by a taskgraph. The performance of the algorithm on the resulting realtime system will meet the specified timing constraints. Some of the hardware components are programmable and others are application specific hardware processors. We propose a powerful MILP (Mixed Integer Linear Program) model with and without functional pipelining. The efficiency of the method is demonstrated with practical examples. 1 Introduction One important task of a hardware/software codesign is to map different tasks of an algorithm onto hardware or software components. Some components are programmable and others are application specific hardware processors. The aim of the codesign is to design a heterogeneous and loosely coupled multiprocessor system, which violates no timing constraints while performing the underly...
Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions, preprint arXiv:1112.1392 available on arXiv
, 2011
"... We study the problem of sampling high and infinite dimensional target measures arising in applications such as conditioned diffusions and inverse problems. We focus on those that arise from approximating measures on Hilbert spaces defined via a density with respect to a Gaussian reference measure. W ..."
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Cited by 20 (8 self)
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We study the problem of sampling high and infinite dimensional target measures arising in applications such as conditioned diffusions and inverse problems. We focus on those that arise from approximating measures on Hilbert spaces defined via a density with respect to a Gaussian reference measure. We consider the MetropolisHastings algorithm that adds an acceptreject mechanism to a Markov chain proposal in order to have the target measure as an ergodic invariant measure. We focus on cases where the proposal is either a Gaussian random walk (RWM) with covariance equal to that of the reference measure or an OrnsteinUhlenbeck proposal (pCN) for which the reference measure is invariant. Previous results in terms of scaling and diffusion limits suggested that the pCN has a convergence rate that is independent of the dimension while the RWM method has undesirable dimensiondependent behaviour. We confirm this claim by showing dimensionindependent Wasserstein spectral gap for pCN algorithm for a large class of target measures. In our setting this Wasserstein spectral gap implies an L2spectral gap. We use both spectral gaps to show that the ergodic average satisfies a strong law of large numbers, the central limit theorem and nonasymptotic bounds on the mean square error, all dimension independent. In contrast we show that the RWM algorithm applied to the reference measures degenerates as the dimension tends to infinity. 1
Population Markov Chain Monte Carlo
 Machine Learning
, 2003
"... Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima ..."
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Cited by 14 (2 self)
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Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search algorithms: a MetropolisHastings Sampler (MHS), an Evolutionary Algorithm (EA), and a new hybrid algorithm called Population Markov Chain Monte Carlo, or popMCMC. PopMCMC uses statistical information from a population of MHSs to inform the proposal distributions for individual samplers in the population. Experimental results show that popMCMC and EAs learn more efficiently than the MHS with no information exchange. Populations of MCMC samplers exhibit more diversity than populations evolving according to EAs not satisfying physicsinspired local reversibility conditions. KEY WORDS: Markov Chain Monte Carlo, MetropolisHastings Algorithm, Graphical Probabilistic Models, Bayesian Networks, Bayesian Learning, Evolutionary Algorithms Machine Learning MCMC Issue 1 5/16/01 1.
Evaluation of the very low BER of FEC codes using dual adaptive importance sampling
 Comm. Lett
, 2005
"... Abstract — We evaluate the errorcorrecting performance of a lowdensity paritycheck (LDPC) code in an AWGN channel using a novel dual adaptive importance sampling (DAIS) technique based on multicanonical Monte Carlo (MMC) simulations, that allows us to calculate bit error rates as low as 10−19 fo ..."
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Cited by 10 (2 self)
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Abstract — We evaluate the errorcorrecting performance of a lowdensity paritycheck (LDPC) code in an AWGN channel using a novel dual adaptive importance sampling (DAIS) technique based on multicanonical Monte Carlo (MMC) simulations, that allows us to calculate bit error rates as low as 10−19 for a (96, 50) LDPC code without a priori knowledge of how to bias. Our results agree very well with standard MC simulations, as well as the union bound for the code. Index Terms — Very low BER, multicanonical Monte Carlo, importance sampling, LDPC codes. I.
Minimum Message Length Clustering Using Gibbs Sampling
 16 TH INTERNATIONAL CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, (UAI
, 2000
"... The KMeans and EM algorithms are popular in clustering and mixture modeling due to their simplicity and ease of implementation. However, they have several significant limitations. Both converge to a local optimum of their respective objective functions (ignoring the uncertainty in the model s ..."
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Cited by 7 (3 self)
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The KMeans and EM algorithms are popular in clustering and mixture modeling due to their simplicity and ease of implementation. However, they have several significant limitations. Both converge to a local optimum of their respective objective functions (ignoring the uncertainty in the model space), require the apriori specification of the number of classes/clusters, and are inconsistent. In this work we overcome these limitations by using the Minimum Message Length (MML) principle and a variation to the KMeans /EM observation assignment and parameter calculation scheme. We maintain the simplicity of these approaches while constructing a Bayesian mixture modeling tool that samples/searches the model space using a Markov Chain Monte Carlo (MCMC) sampler known as a Gibbs sampler. Gibbs
A TokenBased Scheduling Scheme for WLANs Supporting Voice/Data Traffic and its Performance Analysis
 IEEE Transactions on Wireless Communications
, 2008
"... Abstract—Most of the existing medium access control (MAC) protocols for wireless local area networks (WLANs) provide prioritized access by adjusting the contention window sizes or interframe spaces for different traffic classes. Those MAC protocols can only provide statistical priority access and li ..."
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Cited by 5 (0 self)
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Abstract—Most of the existing medium access control (MAC) protocols for wireless local area networks (WLANs) provide prioritized access by adjusting the contention window sizes or interframe spaces for different traffic classes. Those MAC protocols can only provide statistical priority access and limited service differentiation. In this paper, a novel tokenbased scheduling scheme is proposed for a fullyconnected WLAN that supports both voice and data traffic. The proposed scheme can provide guaranteed priority access to voice traffic and, at the same time, provide more precise and quantitative service differentiation for data traffic, which provides great flexibility and facility to the network service provider for service class management. Simulation results demonstrate that the proposed scheme can guarantee a small delay for voice traffic. For data traffic, it can effectively achieve proportional differentiation among different classes, while achieving fair resource sharing within the same class. In addition, compared with a contention based scheme and a centralized polling scheme, the proposed scheme significantly improves the channel utilization by avoiding collisions (in the contention based scheme) and the polling overhead (in the polling scheme). The performance analysis of the proposed scheme is also presented. The accuracy of the analytical results is verified by computer simulations. Index Terms—WLAN, token, priority access, class differentiation, MetropolisHasting. I.
SemiSupervised Learning – A Statistical Physics Approach
 In “Learning with Partially Classified Training Data” – ICML05 workshop
, 2005
"... We present a novel approach to semisupervised learning which is based on statistical physics. Most of the former work in the field of semisupervised learning classifies the points by minimizing a certain energy function, which corresponds to a minimal kway cut solution. In contrast to these method ..."
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Cited by 5 (0 self)
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We present a novel approach to semisupervised learning which is based on statistical physics. Most of the former work in the field of semisupervised learning classifies the points by minimizing a certain energy function, which corresponds to a minimal kway cut solution. In contrast to these methods, we estimate the distribution of classifications, instead of the sole minimal kway cut, which yields more accurate and robust results. Our approach may be applied to all energy functions used for semisupervised learning. The method is based on sampling using a Multicanonical Markov chain MonteCarlo algorithm, and has a straightforward probabilistic interpretation, which allows for soft assignments of points to classes, and also to cope with yet unseen class types. The suggested approach is demonstrated on a toy data set and on two reallife data sets of gene expression. 1.