Optimized Execution of a Numerical Weather Forecast Model in a Cloud Cluster

IF 1.5 4区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Concurrency and Computation-Practice & Experience Pub Date : 2025-01-16 DOI:10.1002/cpe.8374

Mateus S. de Melo, Roberto Pinto Souto, Lúcia M. A. Drummond

{"title":"Optimized Execution of a Numerical Weather Forecast Model in a Cloud Cluster","authors":"Mateus S. de Melo, Roberto Pinto Souto, Lúcia M. A. Drummond","doi":"10.1002/cpe.8374","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This study proposes strategies to reduce the financial cost of using cloud clusters, through Amazon Web Services (AWS) ParallelCluster, to run the weather forecast model Brazilian developments on the Regional Atmospheric Modeling System (BRAMS). We developed an instance selection algorithm that obtains and compares the costs of various instance types in different regions and markets, recommending those with the lowest costs. If the suggested instance is a Spot instance and is revoked by the cloud provider, the proposed strategy resumes the application execution from a pre-recorded checkpoint by rescheduling it on On-Demand instances. This study also presents a detailed analysis of BRAMS execution across various instance architectures and proposes a novel three-queue architecture for managing BRAMS execution on On-Demand and Spot instances within AWS ParallelCluster. The results obtained from small and large spatial domains executed in AWS ParallelCluster using the proposed strategies show that adopting a cloud cluster is a promising alternative for this type of High-Performance Computing application, compared with execution on a supercomputer.</p>\n </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 3","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Concurrency and Computation-Practice & Experience","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cpe.8374","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}

引用次数: 0

Abstract

This study proposes strategies to reduce the financial cost of using cloud clusters, through Amazon Web Services (AWS) ParallelCluster, to run the weather forecast model Brazilian developments on the Regional Atmospheric Modeling System (BRAMS). We developed an instance selection algorithm that obtains and compares the costs of various instance types in different regions and markets, recommending those with the lowest costs. If the suggested instance is a Spot instance and is revoked by the cloud provider, the proposed strategy resumes the application execution from a pre-recorded checkpoint by rescheduling it on On-Demand instances. This study also presents a detailed analysis of BRAMS execution across various instance architectures and proposes a novel three-queue architecture for managing BRAMS execution on On-Demand and Spot instances within AWS ParallelCluster. The results obtained from small and large spatial domains executed in AWS ParallelCluster using the proposed strategies show that adopting a cloud cluster is a promising alternative for this type of High-Performance Computing application, compared with execution on a supercomputer.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Concurrency and Computation-Practice & Experience 工程技术-计算机：理论方法

CiteScore

5.00

自引率

10.00%

发文量

664

审稿时长

9.6 months

期刊介绍： Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.