Journal ArticleDOI
Statistical prediction of task execution times through analytic benchmarking for scheduling in a heterogeneous environment
TLDR
A method for estimating task execution times is presented in order to facilitate dynamic scheduling in a heterogeneous metacomputing environment and predicts the execution time as a function of several parameters of the input data.Abstract:
In this paper, a method for estimating task execution times is presented in order to facilitate dynamic scheduling in a heterogeneous metacomputing environment. Execution time is treated as a random variable and is statistically estimated from past observations. This method predicts the execution time as a function of several parameters of the input data and does not require any direct information about the algorithms used by the tasks or the architecture of the machines. Techniques based upon the concept of analytic benchmarking/code profiling are used to characterize the performance differences between machines, allowing observations from dissimilar machines to be used when making a prediction. Experimental results are presented which use actual execution time data gathered from 16 heterogeneous machines.read more
Citations
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Journal ArticleDOI
A game-theoretic method of fair resource allocation for cloud computing services
TL;DR: A QoS constrained resource allocation problem is considered, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation.
Proceedings ArticleDOI
Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads
TL;DR: This work analyzes and proposes a binary integer program formulation of the scheduling problem and finds that this approach results in a tractable solution for scheduling applications in the public cloud, but that the same method becomes much less feasible in a hybrid cloud setting due to very high solve time variances.
Book
Distributed and Cloud Computing: From Parallel Processing to the Internet of Things
TL;DR: This book will teach you how to create high-performance, scalable, reliable systems, providing comprehensive coverage of distributed and cloud computing, including: Facilitating management, debugging, migration, and disaster recovery through virtualization
Proceedings ArticleDOI
Task execution time modeling for heterogeneous computing systems
TL;DR: A coefficient-of-variation-based ETC generation method is proposed, which provides a greater control over the spread of values in any given row or column of the ETC matrix than the range-based method.
Proceedings ArticleDOI
Improved cost-based algorithm for task scheduling in cloud computing
S. Selvarani,G. Sudha Sadhasivam +1 more
TL;DR: The proposed scheduling approach in cloud employs an improved cost-based scheduling algorithm for making efficient mapping of tasks to available resources in cloud that measures both resource cost and computation performance and improves the computation/communication ratio.
References
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Book
Applied Nonparametric Regression
TL;DR: This chapter discusses smoothing in high Dimensions, Investigating multiple regression by additive models, and incorporating parametric components and alternatives.
Journal ArticleDOI
Applied Nonparametric Regression.
Peter M. Robinson,W. Haerdle +1 more
TL;DR: Applied Nonparametric Regression as mentioned in this paper is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable, including kernel smoothing, spline smoothing and orthogonal polynomials.
Book
Probability, Random Processes, and Estimation Theory for Engineers
Henry Stark,John W. Woods +1 more
TL;DR: This chapter discusses the foundations of estimation and decision theory, and some examples show how the model derived recently in [Bouchut-Boyaval, M3AS (23) 2013] can be modified for flows on rugous topographies.
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