A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds
Journal of Grid Computing
Dimensioning the virtual cluster for parallel scientific workflows in clouds
Proceedings of the 4th ACM workshop on Scientific cloud computing
Designing a parallel cloud based comparative genomics workflow to improve phylogenetic analyses
Future Generation Computer Systems
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Cloud computing has emerged as a new paradigm that enables scientists to benefit from several distributed resources such as hardware and software. Clouds poses as an opportunity for scientists that need high performance computing infrastructure to execute their scientific experiments. Most of the experiments modeled as scientific workflows manage the execution of several activities and work with large amounts of data. In this way parallel techniques are often a key factor. Parallelizing a scientific workflow in the cloud environment is not trivial. One of the complex tasks is to define the number and types of virtual machines and to design the parallel execution strategy. Due to the number of options for configuring an environment it is a hard task to do it manually and it may produce negative impact on performance. This paper initially proposes a cost model based on concepts of quality of service (QoS) in clouds to help determining an adequate configuration of the environment according to restrictions imposed by scientists.