Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms

dc.contributor.authorWang, Jianwu
dc.contributor.authorKorambath, Prakashan
dc.contributor.authorAltintas, Ilkay
dc.contributor.authorDavis, Jim
dc.contributor.authorCrawl, Daniel
dc.date.accessioned2024-02-12T21:40:30Z
dc.date.available2024-02-12T21:40:30Z
dc.date.issued2014-06-06
dc.description.abstractWith more and more workflow systems adopting cloud as their e xecution environment, it becomes increasingly challenging on how to effic iently manage various workflows, v irtual machines (VMs) and workflow e xecution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) a rchitecture with independent services. A core part of the architecture is how to efficiently respond continuous workflo w requests from users and schedule their e xecutions in the c loud. Based on diffe rent targets, we p ropose four heuristic workflo w scheduling a lgorith ms for the WFaaS architecture, and analy ze the differences and best usages of the algorith ms in terms of performance, cost and the price/performance ratio via experimental studies.
dc.description.sponsorshipThis work is supported by DOE grant DE-EE0005763 : “Industrial Scale Demonstration of Smart Manufacturing Achieving Transformational Energy Productivity Gains”, NSF grant DBI-1062565: Workflow as a Service in the Cloud J.Wang, P. Korambath, I. Altintas, J. Davis and D. Crawl “bioKepler: A Comprehensive Bioinformatics Scientific Workflow Module for Distributed Analysis of Large-Scale Biological Data”, and NSF grant 1331615: “WIFIRE: A Scalable Data-Driven Monitoring, Dynamic Prediction and Resilience Cyberinfrastructure for Wildfires ”.
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S1877050914002269
dc.format.extent11 pages
dc.genrejournal articles
dc.identifier.citationWang, Jianwu, Prakashan Korambath, Ilkay Altintas, Jim Davis, and Daniel Crawl. “Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms.” Procedia Computer Science, 2014 International Conference on Computational Science, 29 (January 1, 2014): 546–56. https://doi.org/10.1016/j.procs.2014.05.049.
dc.identifier.urihttps://doi.org/10.1016/j.procs.2014.05.049
dc.identifier.urihttp://hdl.handle.net/11603/31605
dc.language.isoen_US
dc.publisherElsevier
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Center for Accelerated Real Time Analysis
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Data Science
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectUMBC Big Data Analytics Lab
dc.titleWorkflow as a Service in the Cloud: Architecture and Scheduling Algorithms
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
1-s2.0-S1877050914002269-main.pdf
Size:
888.81 KB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description: