Elastic resource allocation in the cloud

Jieqian Wu; Baojian Zhou; Depei Qian; Ming Xie; Wei Chen

doi:10.1109/CSE.2013.198

Elastic resource allocation in the cloud

Jieqian Wu
, Baojian Zhou
, Depei Qian
, Ming Xie
, Wei Chen

School of Computer Science and Engineering

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

In traditional cloud computing, elastic computing can be provided by increasing or decreasing the number of virtual machines (VM). However, the resource needed by applications change swiftly. This coarse-grained resource allocation approach is not a perfect way to satisfy the demand of the workload in short term. In this paper, we present a fine-grained resource allocation method in virtualized servers. This method uses a real-time control model in order to meet the demands of varying workload by sizing the VMs dynamically. We compare, evaluate, and analyze this approach. Experimental results confirm that this approach guarantees performance and resource efficiency for applications.

Original language	English
Pages	1338-1342
Number of pages	5
DOIs	https://doi.org/10.1109/CSE.2013.198
State	Published - 2013
Event	2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013 - Sydney, NSW, Australia Duration: 3 Dec 2013 → 5 Dec 2013

Conference

Conference	2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013
Country/Territory	Australia
City	Sydney, NSW
Period	3/12/13 → 5/12/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 12 Responsible Consumption and Production

Keywords

Cloud
Elastic resource allocation
Resource management
Virtualization

Access to Document

10.1109/CSE.2013.198

Cite this

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title = "Elastic resource allocation in the cloud",

abstract = "In traditional cloud computing, elastic computing can be provided by increasing or decreasing the number of virtual machines (VM). However, the resource needed by applications change swiftly. This coarse-grained resource allocation approach is not a perfect way to satisfy the demand of the workload in short term. In this paper, we present a fine-grained resource allocation method in virtualized servers. This method uses a real-time control model in order to meet the demands of varying workload by sizing the VMs dynamically. We compare, evaluate, and analyze this approach. Experimental results confirm that this approach guarantees performance and resource efficiency for applications.",

keywords = "Cloud, Elastic resource allocation, Resource management, Virtualization",

author = "Jieqian Wu and Baojian Zhou and Depei Qian and Ming Xie and Wei Chen",

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doi = "10.1109/CSE.2013.198",

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note = "2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013 ; Conference date: 03-12-2013 Through 05-12-2013",

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AU - Wu, Jieqian

AU - Zhou, Baojian

AU - Qian, Depei

AU - Xie, Ming

AU - Chen, Wei

PY - 2013

Y1 - 2013

N2 - In traditional cloud computing, elastic computing can be provided by increasing or decreasing the number of virtual machines (VM). However, the resource needed by applications change swiftly. This coarse-grained resource allocation approach is not a perfect way to satisfy the demand of the workload in short term. In this paper, we present a fine-grained resource allocation method in virtualized servers. This method uses a real-time control model in order to meet the demands of varying workload by sizing the VMs dynamically. We compare, evaluate, and analyze this approach. Experimental results confirm that this approach guarantees performance and resource efficiency for applications.

AB - In traditional cloud computing, elastic computing can be provided by increasing or decreasing the number of virtual machines (VM). However, the resource needed by applications change swiftly. This coarse-grained resource allocation approach is not a perfect way to satisfy the demand of the workload in short term. In this paper, we present a fine-grained resource allocation method in virtualized servers. This method uses a real-time control model in order to meet the demands of varying workload by sizing the VMs dynamically. We compare, evaluate, and analyze this approach. Experimental results confirm that this approach guarantees performance and resource efficiency for applications.

KW - Cloud

KW - Elastic resource allocation

KW - Resource management

KW - Virtualization

UR - https://www.scopus.com/pages/publications/84900361119

U2 - 10.1109/CSE.2013.198

DO - 10.1109/CSE.2013.198

M3 - 论文

AN - SCOPUS:84900361119

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EP - 1342

T2 - 2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013

Y2 - 3 December 2013 through 5 December 2013

ER -

Elastic resource allocation in the cloud

Abstract

Conference

UN SDGs

Keywords

Access to Document

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