Solis Moreno, I, Garraghan, P, Townend, P et al. (1 more author) (2014) Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. IEEE Transactions on Cloud Computing, 2 (2). pp. 208-221.
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Editors: |
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Keywords: | Cloud computing; workload characterization; cloud computing simulation; workload modeling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Oct 2014 12:37 |
Last Modified: | 20 Jun 2021 08:37 |
Published Version: | http://dx.doi.org/10.1109/TCC.2014.2314661 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/TCC.2014.2314661 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:80618 |