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Cloud Marginal Resource Allocation: A Decision Support Model

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Abstract

One of the significant challenges for cloud providers is how to manage resources wisely and how to form a viable service level agreement (SLA) with consumers to avoid any violation or penalties. Some consumers make an agreement for a fixed amount of resources, these being the required resources that are needed to execute its business. Consumers may need additional resources on top of these fixed resources, known as– marginal resources that are only consumed and paid for in case of an increase in business demand. In such contracts, both parties agree on a pricing model in which a consumer pays upfront only for the fixed resources and pays for the marginal resources when they are used. A marginal resource allocation is a challenge for service provider particularly small- to medium-sized service providers as it can affect the usage of their resources and consequently their profits. This paper proposes a novel marginal resource allocation decision support model to assist cloud providers to manage the cloud SLAs before its execution, covering all possible scenarios, including whether a consumer is new or not, and whether the consumer requests the same or different marginal resources. The model relies on the capabilities of the user-based collaborative filtering method with an enhanced top-k nearest neighbor algorithm and a fuzzy logic system to make a decision. The proposed framework assists cloud providers manage their resources in an optimal way and avoid violations or penalties. Finally, the performance of the proposed model is shown through a cloud scenario which demonstrates that our proposed approach can assists cloud providers to manage their resources wisely to avoid violations.

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Authors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Walayat Hussain

  2. School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Osama Sohaib & Mohsen Naderpour

  3. Computing Center, Shanghai University, Shanghai, China

    Honghao Gao

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  1. Walayat Hussain
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  2. Osama Sohaib
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  3. Mohsen Naderpour
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  4. Honghao Gao
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Correspondence to Honghao Gao.

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Hussain, W., Sohaib, O., Naderpour, M. et al. Cloud Marginal Resource Allocation: A Decision Support Model. Mobile Netw Appl 25, 1418–1433 (2020). https://doi.org/10.1007/s11036-019-01457-7

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