A heuristic to optimize the execution cost of distributed seismic processing programs on the cloud

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019A heuristic to optimize the execution cost of distributed seismic processing programs on the cloudAuthors: Nicholas T. OkitaTiago A. CoimbraMartin TygelEdson BorinNicholas T. OkitaCEPETRO/UNICAMPSearch for more papers by this author, Tiago A. CoimbraCEPETRO/UNICAMPCCES/CEPIDSearch for more papers by this author, Martin TygelCCES/CEPIDSearch for more papers by this author, and Edson BorinCEPETRO/UNICAMPIC/UNICAMSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216462.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractNowadays, the computational cloud shows itself as an alternative to the traditional clusters, delivering high-performance virtual machines on demand and working on a pay-as-yougo basis, in which the user only needs to pay by the time of use. Nonetheless, inappropriate virtual machines selections can lead to unexpected billings and unacceptable performance. Therefore, this work has the objective to simplify the instance selection process, empowering the user to select how much they are willing to spend to process a data set in the Amazon Web Services (AWS) computational cloud and minimizing the execution time for that budget. To accomplish this task, we present a heuristic that auto-adjusts a cluster on the computational cloud to fit the user requested budget. By making smart choices, we assume that the heuristic will be able to execute the program in the shortest time without spending more than the desired. Our experiments have two steps for analysis: Firstly, we use a simulator to test our heuristic. Secondly, we use AWS cloud infrastructure to process a real dataset. The obtained results by our experiments are promising; they show the heuristic worked well both in simulated and real-world scenarios, delivering expected execution times for the input budgets and near optimum-minimal cost.Presentation Date: Wednesday, September 18, 2019Session Start Time: 8:30 AMPresentation Start Time: 11:00 AMLocation: 214DPresentation Type: OralKeywords: cloud computing, algorithm, economics, processing, programmingPermalink: https://doi.org/10.1190/segam2019-3216462.1FiguresReferencesRelatedDetailsCited byHigh‐performance computing strategies for seismic‐imaging software on the cluster and cloud‐computing environments12 November 2021 | Geophysical Prospecting, Vol. 70, No. 13D seismic data processing using cloud computingJournal of the Japanese Association for Petroleum Technology, Vol. 87, No. 1CLAP-Bot: a framework for automatic optimization of high-performance elastic applications on the CloudsFaster and cheaper: How graphics processing units on spot-market instances minimize turnaround time and budgetNicholas T. Okita and Tiago A. Coimbra20 January 2021 | Interpretation, Vol. 9, No. 1Selecting Efficient Cloud Resources for HPC Workloads2 December 2019 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Nicholas T. Okita, Tiago A. Coimbra, Martin Tygel, and Edson Borin, (2019), "A heuristic to optimize the execution cost of distributed seismic processing programs on the cloud," SEG Technical Program Expanded Abstracts : 4004-4008. https://doi.org/10.1190/segam2019-3216462.1 Plain-Language Summary Keywordscloud computingalgorithmeconomicsprocessingprogrammingPDF DownloadLoading ...