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Scheduling algorithms for efficient execution of stream workflow applications in multicloud environments

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dc.contributor.authorBarika, M.
dc.contributor.authorGarg, S.
dc.contributor.authorChan, A.
dc.contributor.authorCalheiros, R.N.
dc.date.issued2022
dc.description.abstractBig data processing applications are becoming more and more complex. They are no more monolithic in nature but instead they are composed of decoupled analytical processes in the form of a workflow. One type of such workflow applications is stream workflow application, which integrates multiple streaming big data applications to support decision making. Each analytical component of these applications runs continuously and processes data streams whose velocity will depend on several factors such as network bandwidth and processing rate of parent analytical component. As a consequence, the execution of these applications on cloud environments requires advanced scheduling techniques that adhere to end user's requirements in terms of data processing and deadline for decision making. In this paper, we propose two Multicloud scheduling and resource allocation techniques for efficient execution of stream workflow applications on Multicloud environments while adhering to workflow application and user performance requirements and reducing execution cost. Results showed that the proposed genetic algorithm is an adequate and effective for all experiments.
dc.identifier.citationIEEE Transactions on Services Computing, 2022; 15(2):860-875
dc.identifier.doi10.1109/TSC.2019.2963382
dc.identifier.issn1939-1374
dc.identifier.issn1939-1374
dc.identifier.urihttps://hdl.handle.net/11541.2/146692
dc.language.isoen
dc.publisherIEEE
dc.relation.fundingAustralian Government Research Training Program (RTP) Scholarship
dc.rightsCopyright 2019 IEEE Access Condition Notes: Accepted manuscript available open access
dc.source.urihttps://doi.org/10.1109/TSC.2019.2963382
dc.subjectbig data
dc.subjectstream workflow
dc.subjectscheduling
dc.subjectgreedy algorithm
dc.subjectgenetic algorithm
dc.titleScheduling algorithms for efficient execution of stream workflow applications in multicloud environments
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12299273010001831 13299342680001831 Open Access Postprint
ror.mmsid9916505404101831

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