Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/38886
Title: PEESOS-Cloud: a workload-aware architecture for performance evaluation in service-oriented systems
Authors: Ferreira, C. H. G.
Nunes, L. H.
Pereira Jr., L. A.
Nakamura, L. H.
Estrella, J. C.
Reiff-Marganiec, Stephan
First Published: 1-Sep-2016
Presented at: 2016 IEEE World Congress on Services (SERVICES), San Francisco, CA.
Start Date: 27-Jun-2016
End Date: 2-Jul-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: 2016 IEEE World Congress on Services (SERVICES), San Francisco, CA, 2016, pp. 118-125.
Abstract: It is a challenging task to ensure quality in service-oriented systems deployed in cloud computing owing to the dynamicity of its environment. Many approaches have been adopted to identify and evaluate bottlenecks and problems in performance. The most common scenario consists of distributed systems that use a workload capable of enabling clients to exploit the target system in different operational conditions. However, one requirement that tends to be overlooked is to determine how the workload is executed, as software and hardware faults can lead to its mischaracterization. In this paper, a number of problems in the workload generation have been identified and summarized. A new architecture, called PEESOS-Cloud, is proposed which allows these services to be evaluated as well as to improve the ability of the workload so that it conforms with its described characteristics. Experiments in a cloud environment were conducted to show how PEESOS-Cloud works and validate its capabilities. Our experiment also showed that the mischaracterization of the workload leads to poor results, whereas an workload-aware implementation leads to a better performance evaluation.
DOI Link: 10.1109/SERVICES.2016.25
ISBN: 978-1-5090-2617-3
Links: http://ieeexplore.ieee.org/abstract/document/7557407/
http://hdl.handle.net/2381/38886
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: Creative Commons “Attribution Non-Commercial No Derivatives” licence CC BY-NC-ND, further details of which can be found via the following link: http://creativecommons.org/licenses/by-nc-nd/4.0/ Archived with reference to SHERPA/RoMEO and publisher website.
Appears in Collections:Conference Papers & Presentations, Dept. of Computer Science

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