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Time Division Dominant Resource Allocation in Cloud Environments Open Access


Other title
Cloud Computing
Resource Allocation
Type of item
Degree grantor
University of Alberta
Author or creator
Zhang, Xu
Supervisor and department
Niu, Di (Electrical and Computer Engineering)
Jiang, Hai (Electrical and Computer Engineering)
Examining committee member and department
Niu, Di (Electrical and Computer Engineering)
Jing, Yindi (Electrical and Computer Engineering)
Tellambura, Chintha (Electrical and Computer Engineering)
Jiang, Hai (Electrical and Computer Engineering)
Department of Electrical and Computer Engineering
Date accepted
Graduation date
Master of Science
Degree level
Providing service in cloud is a popular trend nowadays. Users request resources from a shared pool in a framework like Hadoop, and the Hadoop fair scheduler controls the progress of tasks from users. In cloud, usually multiple resource types are available. The heterogeneous user demands and competition between users in cloud make the fairness control of multiple-resource allocation much more complicated than the single-resource allocation. A recent work proposed a Dominant Resource Fairness (DRF) method for multiple-resource allocation, which has been included in Hadoop Next-Generation. However, DRF may not lead to desired fairness performance as a result of the indivisibility of the user demands. This thesis proposes a Time Division Allocation (TDA) method with time slots for two users. The TDA method allocates resources over the time slots and each time slot is assigned a different allocation. By adjusting the length of the time slots and the resource allocations, TDA method can achieve a global optimal max-min fairness. A further study shows that two time slots are sufficient to achieve optimality. Besides the analysis about fairness, this thesis also explores the theoretical performance bounds for two users. This analysis illustrates the relationship between user demands and the maximum dominant resource share. The evaluation shows that the TDA method performs better than the DRF method.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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