{"id":12585,"date":"2023-07-12T15:44:17","date_gmt":"2023-07-12T13:44:17","guid":{"rendered":"https:\/\/new.sano.science\/?post_type=research&#038;p=12585"},"modified":"2024-01-05T13:57:38","modified_gmt":"2024-01-05T12:57:38","slug":"algorithms-for-cost-and-deadline-constrained-provisioning-for-scientific-workflow-ensembles-in-iaas-clouds","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/algorithms-for-cost-and-deadline-constrained-provisioning-for-scientific-workflow-ensembles-in-iaas-clouds\/","title":{"rendered":"Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds\u00a0"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Malawski, Maciej; Juve, Gideon; Deelman, Ewa; Nabrzyski, Jarek<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Large-scale applications expressed as scientific workflows are often grouped into ensembles of inter-related workflows. In this paper, we address a new and important problem concerning the efficient management of such ensembles under budget and deadline constraints on Infrastructure as a Service (IaaS) clouds. IaaS clouds are characterized by on-demand resource provisioning capabilities and a pay-per-use model. We discuss, develop, and assess novel algorithms based on static and dynamic strategies for both task scheduling and resource provisioning. We perform the evaluation via simulation using a set of scientific workflow ensembles with a broad range of budget and deadline parameters, taking into account task granularity, uncertainties in task runtime estimations, provisioning delays, and failures. We find that the key factor determining the performance of an algorithm is its ability to decide which workflows in an ensemble to admit or reject for execution. Our results show that an admission procedure based on workflow structure and estimates of task runtimes can significantly improve the quality of solutions.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167739X15000059?via%3Dihub\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>In: Future Generation Computer Systems, vol. 48, pp. 1\u201318, 2015, ISSN: 0167739X.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[16],"class_list":["post-12585","research","type-research","status-publish","hentry","research_type-publications","research_team-extreme-scale-data-and-computing"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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In this paper, we address a new and important problem concerning the efficient management of such ensembles under budget and deadline constraints on Infrastructure as a Service (IaaS) clouds. IaaS clouds are characterized by on-demand resource provisioning capabilities and a pay-per-use model. We discuss, develop, and assess novel algorithms based on static and dynamic strategies for both task scheduling and resource provisioning. We perform the evaluation via simulation using a set of scientific workflow ensembles with a broad range of budget and deadline parameters, taking into account task granularity, uncertainties in task runtime estimations, provisioning delays, and failures. We find that the key factor determining the performance of an algorithm is its ability to decide which workflows in an ensemble to admit or reject for execution. Our results show that an admission procedure based on workflow structure and estimates of task runtimes can significantly improve the quality of solutions.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Large-scale applications expressed as scientific workflows are often grouped into ensembles of inter-related workflows. In this paper, we address a new and important problem concerning the efficient management of such ensembles under budget and deadline constraints on Infrastructure as a Service (IaaS) clouds. IaaS clouds are characterized by on-demand resource provisioning capabilities and a pay-per-use model. We discuss, develop, and assess novel algorithms based on static and dynamic strategies for both task scheduling and resource provisioning. We perform the evaluation via simulation using a set of scientific workflow ensembles with a broad range of budget and deadline parameters, taking into account task granularity, uncertainties in task runtime estimations, provisioning delays, and failures. We find that the key factor determining the performance of an algorithm is its ability to decide which workflows in an ensemble to admit or reject for execution. 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