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Data intensive scientific computing

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thesis
posted on 28.03.2022, 15:52 by Luke Antouny
Applications are continually growing in both size and scale. As a result of this growth the need for distributed processing has become increasingly apparent. Cloud computing offers an effective method of hiring machines to facilitate the execution of specific applications. Users of cloud services have the flexibility to assemble configurations of instances that satisfy their specific computational requirements. Cloud computing also adopts a pay as you go nature ensuring that users only pay for the services that they use. Due to its high cost effectiveness and elasticity, cloud computing has become a desirable platform for many different applications and services. In this thesis we attempt to develop scheduling solutions for a scientific application called Montage. There is an existing cloud native workflow execution framework that has been tested in Amazon EC2.

History

Table of Contents

1. Introduction -- 2. Background and related work -- 3. DEWE v2 : a cloud native workflow execution framework -- 4. Risk management -- 5. Experiments -- 6. Results -- 7. Discussion -- 8. Conclusions -- 9. Future work -- 10. Abbreviations -- 11. Definitions -- Appendices -- Bibliography.

Notes

Empirical thesis. Bibliography: pages 75-76

Awarding Institution

Macquarie University

Degree Type

Thesis bachelor honours

Degree

BSc (Hons), Macquarie University, Faculty of Science and Engineering, School of Engineering

Department, Centre or School

School of Engineering

Year of Award

2016

Principal Supervisor

Young Choon Lee

Rights

Copyright Luke Antouny 2016. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (xv, 76 pages colour illustrations)

Former Identifiers

mq:70346 http://hdl.handle.net/1959.14/1262787

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