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Scheduling large-scale applications on the Grid is a fundamental challenge and is critical to application performance and cost. Large-scale applications typically contain a large number of homogeneous and concurrent activities which are main bottlenecks, but open great potentials for optimization. This paper presents a new formulation of the well-known(More)
It is a large and complex task to design and implement a workflow management system that supports scalable executions of large-scale scientific workflows in distributed and unstable Grid environments. In this paper we describe the Distributed workflow Enactment Engine (DEE) of the ASKALON application development environment for Grid computing. DEE proposes(More)
—As more and more data can be generated at a faster-than-ever rate nowadays, it becomes a challenge to processing large volumes of data for complex data analysis. In order to address performance and cost issues of big data processing on clouds, we present a novel design of adaptive workflow management system which includes an SVM (Support Vector Machine)(More)
—Scheduling multiple large-scale parallel workflow applications on heterogeneous computing systems like hybrid clouds is a fundamental NP-complete problem that is critical to meeting various types of QoS (Quality of Service) requirements. This paper addresses the scheduling problem of large-scale applications inspired from real-world, characterized by a(More)