eCite Digital Repository

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

Citation

Barika, M and Garg, S and Chan, A and Calheiros, RN, Scheduling algorithms for efficient execution of stream workflow applications in multicloud environments, IEEE Transactions on Services Computing pp. 1-14. ISSN 1939-1374 (2019) [Refereed Article]


Preview
PDF
12Mb
  

Copyright Statement

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.

Official URL: https://ieeexplore.ieee.org/document/8946723

DOI: doi:10.1109/TSC.2019.2963382

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 Details

Item Type:Refereed Article
Keywords:cloud computing, stream computing, workflows, big data, stream workflow, scheduling, greedy algorithm, genetic algorithm
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Distributed systems and algorithms
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Barika, M (Mr Mutaz Barika)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Chan, A (Professor Andrew Chan)
ID Code:138492
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-04-09
Last Modified:2021-01-04
Downloads:3 View Download Statistics

Repository Staff Only: item control page