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Empirical prediction models for adaptive resource provisioning in the cloud

Authors:

Anna LiuAuthors Info & Claims

Future Generation Computer Systems, Volume 28, Issue 1

Pages 155 - 162

https://doi.org/10.1016/j.future.2011.05.027

Published: 01 January 2012 Publication History

Abstract

Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands. Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud.

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Published In

cover image Future Generation Computer Systems

Future Generation Computer Systems Volume 28, Issue 1

January, 2012

338 pages

ISSN:0167-739X

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2011.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2012

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Cited By

Glaze NMcNeely TZhu YGleeson MSerr HBhopi RKrishnan S(2024)Lorentz: Learned SKU Recommendation Using Profile DataProceedings of the ACM on Management of Data10.1145/36549522:3(1-25)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654952
Chouliaras SSotiriadis S(2024)Towards constrained optimization of cloud applicationsFuture Generation Computer Systems10.1016/j.future.2023.09.024151:C(100-110)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.future.2023.09.024
Rawat P(2024)Virtual Machine Allocation Using Optimal Resource Management ApproachWireless Personal Communications: An International Journal10.1007/s11277-024-11465-w137:2(1313-1332)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11277-024-11465-w
Quoc KTong VDao CLe TTran D(2024)Boosted regression for predicting CPU utilization in the cloud with periodicityThe Journal of Supercomputing10.1007/s11227-024-06451-980:18(26036-26060)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s11227-024-06451-9
Velu SGill SMurugesan SWu HLi X(2024)CloudAIBus: a testbed for AI based cloud computing environmentsCluster Computing10.1007/s10586-024-04562-927:9(11953-11981)Online publication date: 1-Dec-2024
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Shamsa ZRezaee AAdabi SRahimabadi ARahmani A(2024)A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource supportCluster Computing10.1007/s10586-024-04403-927:4(4281-4320)Online publication date: 11-May-2024
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Stupar IHuljenic D(2023)Model-based cloud service deployment optimisation method for minimisation of application service operational costJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00389-812:1Online publication date: 18-Feb-2023
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Wang LGuo SZhang PYue HLi YWang CCao ZCui D(2023)An Efficient Load Prediction-Driven Scheduling Strategy Model in Container CloudInternational Journal of Intelligent Systems10.1155/2023/59592232023Online publication date: 1-Jan-2023
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Poppe OCastro PLang WLeeka J(2023)Proactive Resource Allocation Policy for Microsoft Azure Cognitive SearchACM SIGMOD Record10.1145/3631504.363151652:3(41-48)Online publication date: 2-Nov-2023
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