40413112
A Low-Cost Rescheduling Policy for Dependent Tasks on Grid Computing Systems
A simple model that can be used for the representation of certain workflows is a directed acyclic graph. Although many heuristics have been proposed to schedule such graphs on heterogeneous environments, most of them assume accurate prediction of computation and communication costs; this limits their direct applicability to a dynamically changing environment, such as the Grid. To deal with this, run-time rescheduling may be needed to improve application performance. This paper presents a low-cost rescheduling policy, which considers rescheduling at a few, carefully selected points in the execution. Yet, this policy achieves performance results, which are comparable with those achieved by a policy that dynamically attempts to reschedule before the execution of every task.
21716413
Advance Reservation Policies for Workflows
Advance reservation of resources has been suggested as a means to provide a certain level of support that meets user expectations with respect to specific job start times in parallel systems. Those expectations may relate to a single job application or an application that consists of a collection of dependent jobs. In the context of Grid computing, applications consisting of dependent tasks become increasingly important, usually known as workflows. This paper focuses on the problem of planning advance reservations for individual tasks of workflow-type of applications when the user specifies a requirement only for the whole workflow application. Two policies to automate advance reservation planning for individual tasks efficiently are presented and evaluated.
7262387
An Experimental Investigation into the Rank Function of the Heterogeneous Earliest Finish Time Scheduling Algorithm
This paper considers the Heterogeneous Earliest Finish Time (HEFT) algorithm for scheduling the tasks of an application, represented by a directed acyclic graph, onto a bounded number of heterogeneous machines. We focus on the appropriate selection of the weight for the nodes and edges of the graph, and experiment with a number of different schemes for computing these weights. Our findings indicate that the length of the schedule produced may be affected significantly by the scheme used, and suggest that the mean value based approach used by HEFT may not be a particularly good choice.
9567759
A hybrid heuristic for DAG scheduling on heterogeneous systems
Summary form only given. This paper is motivated by the observation that different methods to compute the weights of nodes and edges when scheduling DAGs onto heterogeneous machines may lead to significant variations in the generated schedule. To minimize such variations, we present a novel heuristic for DAG scheduling, which is based upon solving a series of independent task scheduling problems. A novel heuristic for the latter problem is also included. Both heuristics compare favourably with other related heuristics.
1436574
Scheduling multiple DAGs onto heterogeneous systems
The problem of scheduling a single DAG onto heterogeneous systems has been studied extensively. In this paper, we focus on the problem of scheduling more than one DAG at the same time onto a set of heterogeneous resources. The aim is not only to optimize the overall makespan, but also to achieve fairness, defined on the basis of the slowdown that each DAG would experience as a result of competing for resources with other DAGs. Two policies particularly focussing to deliver fairness are presented and evaluated along with another four policies that can be used to schedule multiple DAGs.
7720203
A low-cost rescheduling policy for efficient mapping of workflows on grid systems
Workflow management is emerging as an important service in Grid computing. A simple model that can be used for the representation of certain workflows is a directed acyclic graph. Although many heuristics have been proposed to schedule such graphs on heterogeneous environments, most of them assume accurate prediction of computation and communication costs. This limits their direct applicability to a dynamically changing environment, such as the Grid. In this environment, an initial schedule may be built based on estimates, but run-time rescheduling may be needed to improve application performance. This paper presents a low-cost rescheduling policy, which considers rescheduling at a few, carefully selected points during the execution. This policy achieves performance results, which are comparable with those achieved by a policy that dynamically attempts to reschedule before the execution of every task.
18920027
A computational approach for ordering signal transduction pathway components from genomics and proteomics Data
BackgroundSignal transduction is one of the most important biological processes by which cells convert an external signal into a response. Novel computational approaches to mapping proteins onto signaling pathways are needed to fully take advantage of the rapid accumulation of genomic and proteomics information. However, despite their importance, research on signaling pathways reconstruction utilizing large-scale genomics and proteomics information has been limited.ResultsWe have developed an approach for predicting the order of signaling pathway components, assuming all the components on the pathways are known. Our method is built on a score function that integrates protein-protein interaction data and microarray gene expression data. Compared to the individual datasets, either protein interactions or gene transcript abundance measurements, the integrated approach leads to better identification of the order of the pathway components.ConclusionsAs demonstrated in our study on the yeast MAPK signaling pathways, the integration analysis of high-throughput genomics and proteomics data can be a powerful means to infer the order of pathway components, enabling the transformation from molecular data into knowledge of cellular mechanisms.
738592
A General Procedure for Combining Binary Classifiers and Its Performance Analysis
A general procedure for combining binary classifiers for multiclass classification problems with one-against-one decomposition policy is presented in this paper. Two existing schemes, namely the min-max combination and the most-winning combination, may be regarded as its two special cases. We show that the accuracy of the combination procedure will increase and time complexity will decrease as its main parameter increases under a proposed selection algorithm. The experiments verify our main results, and our theoretical analysis gives a valuable criterion for choosing different schemes of combining binary classifiers.
17057402
"I hear the pattern": interactive sonification of geographical data patterns
Interactive sonification (non-speech sound) is a novel strategy to present the geographical distribution patterns of statistical data to vision impaired users. We discuss the design space with dimensions of interaction actions, data representation forms, input devices, navigation structures, and sound feedback encoding. Two interfaces were designed, one using a keyboard and another using a smooth surface touch tablet. A study with three blind users shows that they are able to perceive patterns of 5-category values on both familiar and unknown maps, and learn new map geography, in both interfaces.
11367648
Inferring protein-protein interactions through high-throughput interaction data from diverse organisms
MOTIVATION
Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities.


RESULTS
We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism.


AVAILABILITY
The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.
10817560
Information assessment on predicting protein-protein interactions
BackgroundIdentifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.ResultsOur assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions.ConclusionsIn the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.