Optimizing Biomedical Ultrasound Workflow Scheduling Using Cluster Simulations

Optimizing Biomedical Ultrasound Workflow Scheduling Using Cluster Simulations

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Therapeutic ultrasound plays an increasing role in dealing with oncological diseases, drug delivery and neurostimulation. To maximize the treatment outcome, thorough pre-operative planning using complex numerical models considering patient anatomy is crucial. From the computational point of view, the treatment planning can be seen as the execution of a complex workflow consisting of many different tasks with various computational requirements on a remote cluster or in cloud. Since these resources are precious, workflow scheduling plays an important part in the whole process. This paper describes an extended version of the k-Dispatch workflow management system that uses historical performance data collected on similar workflows to choose suitable amount of computational resources and estimates execution time and cost of particular tasks. This paper also introduces necessary extensions to the Alea cluster simulator that enable the estimation of the queuing and total execution time of the whole workflow. The conjunction of both systems then allows for finegrain optimization of the workflow execution parameters with respect to the current cluster utilization. The experimental results show that this approach is able to reduce the computational time by 26%.

Therapeutic ultrasound plays an increasing role in dealing with oncological diseases, drug delivery and neurostimulation. To maximize the treatment outcome, thorough pre-operative planning using complex numerical models considering patient anatomy is crucial. From the computational point of view, the treatment planning can be seen as the execution of a complex workflow consisting of many different tasks with various computational requirements on a remote cluster or in cloud. Since these resources are precious, workflow scheduling plays an important part in the whole process. This paper describes an extended version of the k-Dispatch workflow management system that uses historical performance data collected on similar workflows to choose suitable amount of computational resources and estimates execution time and cost of particular tasks. This paper also introduces necessary extensions to the Alea cluster simulator that enable the estimation of the queuing and total execution time of the whole workflow. The conjunction of both systems then allows for finegrain optimization of the workflow execution parameters with respect to the current cluster utilization. The experimental results show that this approach is able to reduce the computational time by 26%.

scheduling, workflow, k-Dispatch, simulation, ALEA

scheduling, workflow, k-Dispatch, simulation, ALEA

JAROŠ, M.; KLUSÁČEK, D.; JAROŠ, J.

2021

16.11.2020

Springer Nature Switzerland AG

New Orleans

978-3-030-63170-3

Job Scheduling Strategies for Parallel Processing. JSSPP 2020

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

12326

68

84

17

https://link.springer.com/chapter/10.1007/978-3-030-63171-0_4

JSSCP 2020