Finding a "Kneedle" in a Haystack: Detecting Knee Points in System Behavior

Satopää, Ville A.; Albrecht, Jeannie; Irwin, David; Raghavan, Barath

doi:10.1109/icdcsw.2011.20

Cited by 819 publications

(672 citation statements)

References 12 publications

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“…The number of clusters (k) was chosen based on how average correlation coefficients of expression values of genes in the same clusters changed with cluster numbers or the average size of clusters (i.e., number of genes). The knee [17] in the plot of the correlation coefficient versus the average size, which represents the most drastic change of the balance between the two factors, was used to determine the number of clusters. In our experiment, the genes were initially clustered into 100 clusters (see Additional file 1: Figures S1 and S2).…”

Section: Methodsmentioning

confidence: 99%

Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

et al. 2012

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BackgroundCurrent experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions. In this way, the cell transcriptional machinery can respond optimally to internal or external stimuli. This provides a research opportunity to identify and study co-expressed gene modules whose transcription is controlled by shared gene regulatory networks.ResultsWe developed and integrated a set of computational methods of differential gene expression analysis, gene clustering, gene network inference, gene function prediction, and DNA motif identification to automatically identify differentially co-expressed gene modules, reconstruct their regulatory networks, and validate their correctness. We tested the methods using microarray data derived from soybean cells grown under various stress conditions. Our methods were able to identify 42 coherent gene modules within which average gene expression correlation coefficients are greater than 0.8 and reconstruct their putative regulatory networks. A total of 32 modules and their regulatory networks were further validated by the coherence of predicted gene functions and the consistency of putative transcription factor binding motifs. Approximately half of the 32 modules were partially supported by the literature, which demonstrates that the bioinformatic methods used can help elucidate the molecular responses of soybean cells upon various environmental stresses.ConclusionsThe bioinformatics methods and genome-wide data sources for gene expression, clustering, regulation, and function analysis were integrated seamlessly into one modular protocol to systematically analyze and infer modules and networks from only differential expression genes in soybean cells grown under stress conditions. Our approach appears to effectively reduce the complexity of the problem, and is sufficiently robust and accurate to generate a rather complete and detailed view of putative soybean gene transcription logic potentially underlying the responses to the various environmental challenges. The same automated method can also be applied to reconstruct differentially co-expressed gene modules and their regulatory networks from gene expression data of any other transcriptome.

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Section: Methodsmentioning

confidence: 99%

Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

et al. 2012

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“…As the number of submovements increases, the cost decreases. The optimal number of submovements was determined using an algorithm that detects the point of maximum curvature in the cost-per-submovements curve [17], selecting the minimum number of submovements required for near-asymptotic performance.…”

Section: Methodsmentioning

confidence: 99%

Predicting the initiation of minimum-jerk submovements in three-dimensional target-oriented human arm trajectories

Liao

Kirsch

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Target-oriented human arm trajectories can be represented as a series of summed minimum-jerk submovements. Under this framework, corrections for errors in reaching trajectories could be implemented by adding another submovement to the ongoing trajectory. It has been proposed that a feedback-feedforward error-detection process continuously evaluates trajectory error, but this process initiates corrections at discrete points in time. The present study demonstrates the ability of a feed-forward Artificial Neural Network (ANN) to learn the function of this error-detection process. Experimentally recorded human target-oriented arm trajectories were decomposed into submovements. It was assumed that the parameters of each submovement are known at their onset. Trained on these parameters, for each of three participants, an ANN can predict presence of corrections with sensitivity and specificity > 80%, and can predict their timing with R 2 > 40%.

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“…Alternately, the threshold can be obtained systematically (e.g., using knee detection schemes [39]) or learnt during boot-up phase of an application in the data-center, given application traffic typically ramps up slowly before production workloads are handled. Since the initial threshold can change (e.g., due to changes in request mix), Dealer dynamically updates the threshold using Algorithm 2.…”

Section: Estimating Capacity Of Componentsmentioning

confidence: 99%

Dynamic Request Splitting for Interactive Cloud Applications

Hajjat

Shankaranarayanan

Maltz

et al. 2013

IEEE J. Select. Areas Commun.

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Deploying interactive applications in the cloud is a challenge due to the high variability in performance of cloud services. In this paper, we present Dealer-a system that helps geo-distributed, interactive and multi-tier applications meet their stringent requirements on response time despite such variability. Our approach is motivated by the fact that, at any time, only a small number of application components of large multi-tier applications experience poor performance. Dealer continually monitors the performance of individual components and communication latencies between them to build a global view of the application. In serving any given request, Dealer seeks to minimize user response times by picking the best combination of replicas (potentially located across different data-centers). While Dealer requires modifications to application code, we show the changes required are modest. Our evaluations on two multi-tier applications using real cloud deployments indicate the 90%ile of response times could be reduced by more than a factor of 6 under natural cloud dynamics. Our results indicate the cost of inter-data-center traffic with Dealer is minor, and that Dealer can in fact be used to reduce the overall operational costs of applications by up to 15% by leveraging the difference in billing plans of cloud instances.

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Finding a "Kneedle" in a Haystack: Detecting Knee Points in System Behavior

Cited by 819 publications

References 12 publications

Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

Predicting the initiation of minimum-jerk submovements in three-dimensional target-oriented human arm trajectories

Dynamic Request Splitting for Interactive Cloud Applications

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