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Abstract

Big 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.

Item Type:	Article
Authors/Creators:	Barika, MSM and Garg, S and Chan, A and Calheiros, RN
Keywords:	cloud computing, stream computing, workflows, big data, stream workflow, scheduling, greedy algorithm, genetic algorithm
Journal or Publication Title:	IEEE Transactions on Services Computing
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1939-1374
DOI / ID Number:	https://doi.org/10.1109/TSC.2019.2963382
Copyright Information:	Copyright 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs:	Google Scholar: Garg, S UTAS Profile: Garg, S Google Scholar: Chan, A UTAS Profile: Chan, A
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