Journal ArticleDOI
QBARM: A Queue Theory-Based Adaptive Resource Usage Model
TLDR
The proposed workflow framework then monitors the workflow execution, and utilizes different pricing models to acquire cloud resources according to the change of workflow load, and the cost of workflow execution is reduced.Abstract:
Workflow is becoming a more and more important tool for business operations, scientific research and engineering. Cloud computing provides an elastic, on-demand and high cost-efficient resource allocation model for workflow executions. During workflow execution, the load will change from time to time and therefore, it becomes an interesting topic to optimize resource utilization of workflows in the cloud computing environment. In this paper, a workflow framework is proposed that can adaptively use cloud resources. In detail, after users specify the desired goal to achieve, the proposed workflow framework then monitors the workflow execution, and utilizes different pricing models to acquire cloud resources according to the change of workflow load. In this way, the cost of workflow execution is reduced. .read more
References
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ReportDOI
The NIST Definition of Cloud Computing
Peter Mell,Timothy Grance +1 more
TL;DR: This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
Journal ArticleDOI
Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility
TL;DR: This paper defines Cloud computing and provides the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs), and provides insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA) oriented resource allocation.
Proceedings ArticleDOI
Experiences using cloud computing for a scientific workflow application
TL;DR: This paper describes the experiences running a scientific workflow application developed to process astronomy data released by the Kepler project, a NASA mission to search for Earth-like planets orbiting other stars, and demonstrates how Pegasus was able to support sky computing by executing a single workflow across multiple cloud infrastructures simultaneously.
Proceedings ArticleDOI
Intelligent Workload Factoring for a Hybrid Cloud Computing Model
TL;DR: The core technology of the intelligent workload factoring service is a fast frequent data item detection algorithm, which enables factoring incoming requests not only on volume but also on data content, upon {\em changing} application data popularity.
Journal IssueDOI
Special Issue: The First Provenance Challenge
Luc Moreau,Bertram Ludäscher,Ilkay Altintas,Roger Barga,Shawn Bowers,Steven P. Callahan,George Chin,Ben Clifford,Shirley Cohen,Sarah Cohen-Boulakia,Susan B. Davidson,Ewa Deelman,Luciano Antonio Digiampietri,Ian Foster,Juliana Freire,James Frew,Joe Futrelle,Tara Gibson,Yolanda Gil,Carole Goble,Jennifer Golbeck,Paul Groth,David A. Holland,Sheng Jiang,Jihie Kim,David Koop,Ales Krenek,Timothy M. McPhillips,Gaurang Mehta,Simon Miles,Dominic Metzger,Steve Munroe,James D. Myers,Beth Plale,Norbert Podhorszki,Varun Ratnakar,Emanuele Santos,Carlos Scheidegger,Karen Schuchardt,Margo Seltzer,Yogesh Simmhan,Cláudio T. Silva,Peter Slaughter,Eric G. Stephan,Robert Stevens,Daniele Turi,Huy T. Vo,Michael Wilde,Jun Zhao,Yong Zhao +49 more
TL;DR: A functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed.