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Analysis, modeling and simulation of workload patterns in a large-scale utility cloud

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Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. / Moreno, Ismael Solis; Garraghan, Peter; Townend, Paul; Xu, Jie.

In: IEEE Transactions on Cloud Computing, Vol. 2, No. 2, 01.04.2014, p. 208-221.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Moreno, IS, Garraghan, P, Townend, P & Xu, J 2014, 'Analysis, modeling and simulation of workload patterns in a large-scale utility cloud', IEEE Transactions on Cloud Computing, vol. 2, no. 2, pp. 208-221. https://doi.org/10.1109/TCC.2014.2314661

APA

Moreno, I. S., Garraghan, P., Townend, P., & Xu, J. (2014). Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. IEEE Transactions on Cloud Computing, 2(2), 208-221. https://doi.org/10.1109/TCC.2014.2314661

Vancouver

Moreno IS, Garraghan P, Townend P, Xu J. Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. IEEE Transactions on Cloud Computing. 2014 Apr 1;2(2):208-221. https://doi.org/10.1109/TCC.2014.2314661

Author

Moreno, Ismael Solis ; Garraghan, Peter ; Townend, Paul ; Xu, Jie. / Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. In: IEEE Transactions on Cloud Computing. 2014 ; Vol. 2, No. 2. pp. 208-221.

Bibtex

@article{acc79b1f0cfe4ecca1b4330ff5f22c52,
title = "Analysis, modeling and simulation of workload patterns in a large-scale utility cloud",
abstract = "Understanding the characteristics and patterns of workloads within a Cloud computing environment is critical in order to improve resource management and operational conditions while Quality of Service (QoS) guarantees are maintained. Simulation models based on realistic parameters are also urgently needed for investigating the impact of these workload characteristics on new system designs and operation policies. Unfortunately there is a lack of analyses to support the development of workload models that capture the inherent diversity of users and tasks, largely due to the limited availability of Cloud tracelogs as well as the complexity in analyzing such systems. In this paper we present a comprehensive analysis of the workload characteristics derived from a production Cloud data center that features over 900 users submitting approximately 25 million tasks over a time period of a month. Our analysis focuses on exposing and quantifying the diversity of behavioral patterns for users and tasks, as well as identifying model parameters and their values for the simulation of the workload created by such components. Our derived model is implemented by extending the capabilities of the CloudSim framework and is further validated through empirical comparison and statistical hypothesis tests. We illustrate several examples of this work's practical applicability in the domain of resource management and energy-efficiency.",
keywords = "Computational modeling, Analytical models, Cloud computing, Computer applications, Resource management, Mathematical model",
author = "Moreno, {Ismael Solis} and Peter Garraghan and Paul Townend and Jie Xu",
year = "2014",
month = apr,
day = "1",
doi = "10.1109/TCC.2014.2314661",
language = "English",
volume = "2",
pages = "208--221",
journal = "IEEE Transactions on Cloud Computing",
issn = "2168-7161",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Analysis, modeling and simulation of workload patterns in a large-scale utility cloud

AU - Moreno, Ismael Solis

AU - Garraghan, Peter

AU - Townend, Paul

AU - Xu, Jie

PY - 2014/4/1

Y1 - 2014/4/1

N2 - Understanding the characteristics and patterns of workloads within a Cloud computing environment is critical in order to improve resource management and operational conditions while Quality of Service (QoS) guarantees are maintained. Simulation models based on realistic parameters are also urgently needed for investigating the impact of these workload characteristics on new system designs and operation policies. Unfortunately there is a lack of analyses to support the development of workload models that capture the inherent diversity of users and tasks, largely due to the limited availability of Cloud tracelogs as well as the complexity in analyzing such systems. In this paper we present a comprehensive analysis of the workload characteristics derived from a production Cloud data center that features over 900 users submitting approximately 25 million tasks over a time period of a month. Our analysis focuses on exposing and quantifying the diversity of behavioral patterns for users and tasks, as well as identifying model parameters and their values for the simulation of the workload created by such components. Our derived model is implemented by extending the capabilities of the CloudSim framework and is further validated through empirical comparison and statistical hypothesis tests. We illustrate several examples of this work's practical applicability in the domain of resource management and energy-efficiency.

AB - Understanding the characteristics and patterns of workloads within a Cloud computing environment is critical in order to improve resource management and operational conditions while Quality of Service (QoS) guarantees are maintained. Simulation models based on realistic parameters are also urgently needed for investigating the impact of these workload characteristics on new system designs and operation policies. Unfortunately there is a lack of analyses to support the development of workload models that capture the inherent diversity of users and tasks, largely due to the limited availability of Cloud tracelogs as well as the complexity in analyzing such systems. In this paper we present a comprehensive analysis of the workload characteristics derived from a production Cloud data center that features over 900 users submitting approximately 25 million tasks over a time period of a month. Our analysis focuses on exposing and quantifying the diversity of behavioral patterns for users and tasks, as well as identifying model parameters and their values for the simulation of the workload created by such components. Our derived model is implemented by extending the capabilities of the CloudSim framework and is further validated through empirical comparison and statistical hypothesis tests. We illustrate several examples of this work's practical applicability in the domain of resource management and energy-efficiency.

KW - Computational modeling

KW - Analytical models

KW - Cloud computing

KW - Computer applications

KW - Resource management

KW - Mathematical model

U2 - 10.1109/TCC.2014.2314661

DO - 10.1109/TCC.2014.2314661

M3 - Journal article

VL - 2

SP - 208

EP - 221

JO - IEEE Transactions on Cloud Computing

JF - IEEE Transactions on Cloud Computing

SN - 2168-7161

IS - 2

ER -