Towards Efficient Resource Allocation for Distributed Workflows Under Demand Uncertainties

Friese, Ryan; Halappanavar, Mahantesh; Sathanur, Arun V.; Schram, M.; Kerbyson, Darren J.; Torre, Luís de la

doi:10.1007/978-3-319-77398-8_6

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Resource Allocation1

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Cited by 4 publications

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“…Friese et al 21 proposed a method to compute cost-efficient selection and scheduling of resources under demand uncertainties, but that could be extended to include uncertainties related to resource availability and network performance. The objective function used in Friese's work is to minimize total cost of purchasing slot execution time in cloud computing resources such as Amazon EC2.…”

Section: Resource Allocationmentioning

confidence: 99%

On the use of HCM to develop a resource allocation algorithm for heterogeneous clusters

Soares

Santos

Lobosco

2018

Concurrency and Computation

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Summary This work presents a resource allocation algorithm that considers the characteristics of regular applications to choose the subset of processors, accelerators, and networks that minimize their parallel execution time in a small heterogeneous cluster environment. The resource allocation algorithm uses Heterogeneous Cluster Model (HCM) to estimate the execution time of the parallel applications. The experimental results have shown that, for applications with distinct behaviors, the resource allocation algorithm has successfully chosen the set of resources that minimizes their execution time.

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Section: Resource Allocationmentioning

confidence: 99%