Gokul Madathupalyam Chinnappan , Bharadwaj Veeravalli , Koen Mouthaan , John Wen-Hao Lee
{"title":"对分布式计算基础设施上基于可分割负载范式的合成孔径雷达图像重建多分期调度策略的实验评估","authors":"Gokul Madathupalyam Chinnappan , Bharadwaj Veeravalli , Koen Mouthaan , John Wen-Hao Lee","doi":"10.1016/j.jpdc.2024.104942","DOIUrl":null,"url":null,"abstract":"<div><p>Radar loads, especially Synthetic Aperture Radar (SAR) image reconstruction loads use a large volume of data collected from satellites to create a high-resolution image of the earth. To design near-real-time applications that utilise SAR data, speeding up the image reconstruction algorithm is imperative. This can be achieved by deploying a set of distributed computing infrastructures connected through a network. Scheduling such complex and large divisible loads on a distributed platform can be designed using the Divisible Load Theory (DLT) framework. We performed distributed SAR image reconstruction experiments using the SLURM library on a cloud virtual machine network using two scheduling strategies, namely the Multi-Installment Scheduling with Result Retrieval (MIS-RR) strategy and the traditional EQual-partitioning Strategy (EQS). The DLT model proposed in the MIS-RR strategy is incorporated to make the load divisible. Based on the experimental results and performance analysis carried out using different pixel lengths, pulse set sizes, and the number of virtual machines, we observe that the time performance of MIS-RR is much superior to that of EQS. Hence the MIS-RR strategy is of practical significance in reducing the overall processing time, and cost, and in improving the utilisation of the compute infrastructure. Furthermore, we note that the DLT-based theoretical analysis of MIS-RR coincides well with the experimental data, demonstrating the relevance of DLT in the real world.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Experimental evaluation of a multi-installment scheduling strategy based on divisible load paradigm for SAR image reconstruction on a distributed computing infrastructure\",\"authors\":\"Gokul Madathupalyam Chinnappan , Bharadwaj Veeravalli , Koen Mouthaan , John Wen-Hao Lee\",\"doi\":\"10.1016/j.jpdc.2024.104942\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Radar loads, especially Synthetic Aperture Radar (SAR) image reconstruction loads use a large volume of data collected from satellites to create a high-resolution image of the earth. To design near-real-time applications that utilise SAR data, speeding up the image reconstruction algorithm is imperative. This can be achieved by deploying a set of distributed computing infrastructures connected through a network. Scheduling such complex and large divisible loads on a distributed platform can be designed using the Divisible Load Theory (DLT) framework. We performed distributed SAR image reconstruction experiments using the SLURM library on a cloud virtual machine network using two scheduling strategies, namely the Multi-Installment Scheduling with Result Retrieval (MIS-RR) strategy and the traditional EQual-partitioning Strategy (EQS). The DLT model proposed in the MIS-RR strategy is incorporated to make the load divisible. Based on the experimental results and performance analysis carried out using different pixel lengths, pulse set sizes, and the number of virtual machines, we observe that the time performance of MIS-RR is much superior to that of EQS. Hence the MIS-RR strategy is of practical significance in reducing the overall processing time, and cost, and in improving the utilisation of the compute infrastructure. Furthermore, we note that the DLT-based theoretical analysis of MIS-RR coincides well with the experimental data, demonstrating the relevance of DLT in the real world.</p></div>\",\"PeriodicalId\":54775,\"journal\":{\"name\":\"Journal of Parallel and Distributed Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Parallel and Distributed Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0743731524001060\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Parallel and Distributed Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0743731524001060","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
Experimental evaluation of a multi-installment scheduling strategy based on divisible load paradigm for SAR image reconstruction on a distributed computing infrastructure
Radar loads, especially Synthetic Aperture Radar (SAR) image reconstruction loads use a large volume of data collected from satellites to create a high-resolution image of the earth. To design near-real-time applications that utilise SAR data, speeding up the image reconstruction algorithm is imperative. This can be achieved by deploying a set of distributed computing infrastructures connected through a network. Scheduling such complex and large divisible loads on a distributed platform can be designed using the Divisible Load Theory (DLT) framework. We performed distributed SAR image reconstruction experiments using the SLURM library on a cloud virtual machine network using two scheduling strategies, namely the Multi-Installment Scheduling with Result Retrieval (MIS-RR) strategy and the traditional EQual-partitioning Strategy (EQS). The DLT model proposed in the MIS-RR strategy is incorporated to make the load divisible. Based on the experimental results and performance analysis carried out using different pixel lengths, pulse set sizes, and the number of virtual machines, we observe that the time performance of MIS-RR is much superior to that of EQS. Hence the MIS-RR strategy is of practical significance in reducing the overall processing time, and cost, and in improving the utilisation of the compute infrastructure. Furthermore, we note that the DLT-based theoretical analysis of MIS-RR coincides well with the experimental data, demonstrating the relevance of DLT in the real world.
期刊介绍:
This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing.
The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.