A cloud eco-system : reactive demand control and dynamic pricing methodology
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Chi, Yuanfang
issued: 2015-11-05T03:32:10
issued: 2015
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Resources are limited in capacity. In the meanwhile, over-provisioning of resources resulted in server low utilization could be costly to cloud providers. The underlying reasons of the low utilization are multiple-folds, such as uneven application fit where the application cannot fully utilize the resources allocated or the uncertainty in demand forecasts that is introduced by the dramatically varied demand of the cloud resource between peak and non-peak periods. While many research works are devoted to optimize the resource allocation techniques in the effort of achieving higher server utilization, how to control resource demand so the correct level of resource provisioning can be determined has become the next research question. In this thesis, we introduce a pricing methodology with dynamic pricing that intended to induce desired demand pattern and enhances the revenue of a cloud provider. The proposed pricing methodology encourages cloud tenants, whose requested Virtual Machines (VMs) can be allocated easily, to use more cloud service by offering them lower prices and discouraging cloud tenants, whose requested VMs are difficult to allocate, from using cloud service by charging them higher prices. We study our pricing methodology with a combinatorial optimization algorithm, the Knapsack Algorithm and show that the overall revenue is enhanced through evaluations. Then, to achieve fairness among users, we further perform a case study of our pricing methodology with a multi-resource allocation fairness algorithm, the Heterogeneous Dominant Resource Fairness (DRFH) algorithm. Trace-driven simulation results show that the proposed pricing methodology with DRFH can increase the overall revenue by up to 11.60%. Furthermore, we propose a novel cloud federation system that is cognitive to the dynamic prices as a decision making assistant tool for our pricing methodology. The cloud federation system automatically selects and migrates user tasks to a cloud system that is c…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/55138
note: A Cloud Eco-System: Reactive DemandControl and Dynamic Pricing MethodologybyYuanfang ChiB.A.Sc., The University of British Columbia, 2012A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Electrical and Computer Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)October 2015c© Yuanfang Chi 2015AbstractResources are limited in capacity. In the meanwhile, over-provisioning of resources resulted in serverlow utilization could be costly to cloud providers. The underlying reasons of the low utilization aremultiple-folds, such as uneven application fit where the application cannot fully utilize the resourcesallocated or the uncertainty in demand forecasts that is introduced by the dramatically varied demand ofthe cloud resource between peak and non-peak periods. While many research works are devoted to opti-mize the resource allocation techniques in the effort of achieving higher server utilization, how to controlresource demand so the correct level of resource provisioning can be determined has become the nextresearch question. In this thesis, we introduce a pricing methodology with dynamic pricing that intendedto induce desired demand pattern and enhances the revenue of a cloud provider. The proposed pricingmethodology encourages cloud tenants, whose requested Virtual Machines (VMs) can be allocated eas-ily, to use more cloud service by offering them lower prices and discouraging cloud tenants, whoserequested VMs are difficult to allocate, from using cloud service by charging them higher prices. Westudy our pricing methodology with a combinatorial optimization algorithm, the Knapsack Algorithmand show that the overall revenue is enhanced through evaluations. Then, to achieve fairness amongusers, we further perform a case study of our pricing methodology with a multi-resource allocationfairness algorithm, the Heterogeneous Dominant Resource Fairness (DRFH) algorit…
has type: Thesis/Dissertation
date issued: 2015-11
is shown at: 10.14288/1.0216013
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivs 2.5 Canada
rights URI: http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/55138