Cache Performance Models for Quality of Service Compliance in Storage Clouds

Ernest Sithole, Aaron McConnell, Sally McClean, Gerard Parr, Bryan Scotney, Adrian Moore, David Bustard

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

With the growing popularity of cloud-based data centres as the enterprise IT platform of choice, there is a need for effective management strategies capable of maintaining performance within SLA and QoS parameters when responding to dynamic conditions such as increasing demand. Since current management approaches in the cloud infrastructure, particularly for data-intensive applications, lack the ability to systematically quantify performance trends, static approaches are largely employed in the allocations of resources when dealing with volatile demand in the infrastructure. We present analytical models for characterising cache performance trends at storage cache nodes. Practical validations of cache performance for derived theoretical trends show close approximations between modelled characterisations and measurement results for user request patterns involving private datasets and publicly available datasets. The models are extended to encompass hybrid scenarios based on concurrent requests of both private and public content. Our models have potential for guiding (a) efficient resource allocations during initial deployments of the storage cloud infrastructure and (b) timely interventions during operation in order to achieve scalable and resilient service delivery.
LanguageEnglish
PagesArticle 1-(24 pages)
JournalJournal of Cloud Computing: Advances, Systems and Applications
Volume2
Issue number1
DOIs
Publication statusPublished - Dec 2013

Fingerprint

Quality of service
Resource allocation
Analytical models
Industry
Compliance

Keywords

  • Storage cloud
  • Enterprise applications
  • Cache performance
  • Optimisation

Cite this

@article{162ddaef2fef42c2a951eeb8d8e8df2b,
title = "Cache Performance Models for Quality of Service Compliance in Storage Clouds",
abstract = "With the growing popularity of cloud-based data centres as the enterprise IT platform of choice, there is a need for effective management strategies capable of maintaining performance within SLA and QoS parameters when responding to dynamic conditions such as increasing demand. Since current management approaches in the cloud infrastructure, particularly for data-intensive applications, lack the ability to systematically quantify performance trends, static approaches are largely employed in the allocations of resources when dealing with volatile demand in the infrastructure. We present analytical models for characterising cache performance trends at storage cache nodes. Practical validations of cache performance for derived theoretical trends show close approximations between modelled characterisations and measurement results for user request patterns involving private datasets and publicly available datasets. The models are extended to encompass hybrid scenarios based on concurrent requests of both private and public content. Our models have potential for guiding (a) efficient resource allocations during initial deployments of the storage cloud infrastructure and (b) timely interventions during operation in order to achieve scalable and resilient service delivery.",
keywords = "Storage cloud, Enterprise applications, Cache performance, Optimisation",
author = "Ernest Sithole and Aaron McConnell and Sally McClean and Gerard Parr and Bryan Scotney and Adrian Moore and David Bustard",
year = "2013",
month = "12",
doi = "10.1186/2192-113X-2-1",
language = "English",
volume = "2",
pages = "Article 1--(24 pages)",
journal = "Journal of Cloud Computing: Advances, Systems and Applications",
issn = "2192-113X",
number = "1",

}

Cache Performance Models for Quality of Service Compliance in Storage Clouds. / Sithole, Ernest; McConnell, Aaron; McClean, Sally; Parr, Gerard; Scotney, Bryan; Moore, Adrian; Bustard, David.

In: Journal of Cloud Computing: Advances, Systems and Applications, Vol. 2, No. 1, 12.2013, p. Article 1-(24 pages).

Research output: Contribution to journalArticle

TY - JOUR

T1 - Cache Performance Models for Quality of Service Compliance in Storage Clouds

AU - Sithole, Ernest

AU - McConnell, Aaron

AU - McClean, Sally

AU - Parr, Gerard

AU - Scotney, Bryan

AU - Moore, Adrian

AU - Bustard, David

PY - 2013/12

Y1 - 2013/12

N2 - With the growing popularity of cloud-based data centres as the enterprise IT platform of choice, there is a need for effective management strategies capable of maintaining performance within SLA and QoS parameters when responding to dynamic conditions such as increasing demand. Since current management approaches in the cloud infrastructure, particularly for data-intensive applications, lack the ability to systematically quantify performance trends, static approaches are largely employed in the allocations of resources when dealing with volatile demand in the infrastructure. We present analytical models for characterising cache performance trends at storage cache nodes. Practical validations of cache performance for derived theoretical trends show close approximations between modelled characterisations and measurement results for user request patterns involving private datasets and publicly available datasets. The models are extended to encompass hybrid scenarios based on concurrent requests of both private and public content. Our models have potential for guiding (a) efficient resource allocations during initial deployments of the storage cloud infrastructure and (b) timely interventions during operation in order to achieve scalable and resilient service delivery.

AB - With the growing popularity of cloud-based data centres as the enterprise IT platform of choice, there is a need for effective management strategies capable of maintaining performance within SLA and QoS parameters when responding to dynamic conditions such as increasing demand. Since current management approaches in the cloud infrastructure, particularly for data-intensive applications, lack the ability to systematically quantify performance trends, static approaches are largely employed in the allocations of resources when dealing with volatile demand in the infrastructure. We present analytical models for characterising cache performance trends at storage cache nodes. Practical validations of cache performance for derived theoretical trends show close approximations between modelled characterisations and measurement results for user request patterns involving private datasets and publicly available datasets. The models are extended to encompass hybrid scenarios based on concurrent requests of both private and public content. Our models have potential for guiding (a) efficient resource allocations during initial deployments of the storage cloud infrastructure and (b) timely interventions during operation in order to achieve scalable and resilient service delivery.

KW - Storage cloud

KW - Enterprise applications

KW - Cache performance

KW - Optimisation

U2 - 10.1186/2192-113X-2-1

DO - 10.1186/2192-113X-2-1

M3 - Article

VL - 2

SP - Article 1-(24 pages)

JO - Journal of Cloud Computing: Advances, Systems and Applications

T2 - Journal of Cloud Computing: Advances, Systems and Applications

JF - Journal of Cloud Computing: Advances, Systems and Applications

SN - 2192-113X

IS - 1

ER -