Evaluating Grasp-based cloud dimensioning for comparative genomics: A practical approach

Coutinho, Rafaelli; Drummond, Lúcia Maria de A.; Frota, Yuri; Oliveira, Daniel de; Ocaña, Kary

doi:10.1109/cluster.2014.6968789

Cited by 9 publications

(9 citation statements)

References 26 publications

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“…However, existing cost models are not suitable for the SWfs that have a big part of the sequential workload. For instance, the dynamic approach proposed in [13] ignores the sequential part of the SWf and the cost of provisioning VMs, which may generate VM provisioning plans that yield high cost.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The existing cost models for generating VM provisioning plans [13] are not suitable for SWfs that have some sequential part in their workload. For instance, as presented in [13], the real execution time of two SWfs (SciEvol and SciPhylomics [27]) in Amazon EC2 [1] is two times the estimated time. In addition, some cost models [14] [31] for SWf scheduling cannot be used for generating a proper number of virtual CPUs (CPUs designed to VMs) to instantiate for SWf execution at a multisite cloud.…”

Section: Related Workmentioning

confidence: 99%

“…However, it is only suitable for static computing resources. Coutinho et al [13] propose a GraspCC algorithm to generate a provisioning plan for fragment execution. However, GraspCC relies on the strong assumption that the entire workload of SWfs can be executed in parallel.…”

Section: Related Workmentioning

confidence: 99%

“…α 1 represents the percentage of the workload that can be executed in parallel. ComputingS peedPerCPU 2 represents the average computing performance of each virtual CPU, which is measured by FLOPS (FLoating-point Operations Per Second) [13]. Workload represents the workload of Fragment w f with specific amounts of input data InputData, which can be measured by the number of FLOP (FLoat-point Operations) [13].…”

Section: Datat Rans F Eramountmentioning

confidence: 99%

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Multi-objective scheduling of Scientific Workflows in multisite clouds

Pacitti

Valduriez

et al. 2016

Future Generation Computer Systems

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Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites (or data centers), each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographical distribution of data or available resources among different cloud sites. Therefore, a major problem is how to execute a SWf in a multisite cloud, while reducing execution time and monetary costs. In this paper, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The solution consists of a multi-objective cost model including execution time and monetary costs, a Single Site Virtual Machine (VM) Provisioning approach (SSVP) and ActGreedy, a multisite scheduling approach. We present an experimental evaluation, based on the execution of the SciEvol SWf in Microsoft Azure cloud. The results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms (which we propose by adapting two existing algorithms) and the scheduling time is reasonable compared with genetic and brute-force algorithms. The results also show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach.Work partially funded by EU H2020 Programme and MCTI/RNP-Brazil (HPC4E grant agreement number 689772), CNPq, FAPERJ, and INRIA (MUSIC project), Microsoft\ud (ZcloudFlow project) and performed in the context of the Computational Biology Institute (www.ibc-montpellier.fr). We would like to thank Kary Ocaña for her help in modeling and\ud executing the SciEvol SWf.Peer ReviewedPostprint (author's final draft

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Section: Related Workmentioning

confidence: 99%