Performance Analysis and Configuration Selection for Applications in the Cloud

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dc.contributor.advisorNg, T. S. Eugene
dc.contributor.committeeMemberCox, Alan L.
dc.contributor.committeeMemberJermaine, Christopher M.
dc.creatorLiu, Ruiqi
dc.date.accessioned2016-01-25T15:38:03Z
dc.date.available2016-01-25T15:38:03Z
dc.date.created2015-05
dc.date.issued2015-05-29
dc.date.submittedMay 2015
dc.date.updated2016-01-25T15:38:03Z
dc.description.abstractCloud computing is becoming increasingly popular and widely used in both industries and academia. Making best use of cloud computing resources is critically important. Default resource configurations provided by cloud platforms are often not tailored for applications. Hardware heterogeneity in cloud computers such as Amazon EC2 leads to wide variation in performance, which provides an avenue for research in saving cost and improving performance by exploiting the heterogeneity. In this thesis, I conduct exhaustive measurement studies on Amazon EC2 cloud platforms. I characterize the heterogeneity of resources, and analyze the suitability of different resource configurations for various applications. Measurement results show significant performance diversity across resource configurations of different virtual machine sizes and with different processor types. Diversity in resource capacity is not the only reason for performance diversity; diagnostic measurements reveal that the influence from the cloud provider’s scheduling policy is also an important factor. Furthermore, I propose a nearest neighbor shortlisting algorithm that selects a configuration leading to superior performance for an application by matching the characteristics of the application with that of known benchmark programs. My experimental evaluations show that nearest neighbor greatly reduces the testing overhead since only the shortlisted top configurations rather than all configurations need to be tested; the method achieves high accuracy because the target application chooses the configuration for itself via testing. Even without any test, nearest neighbor is able to obtain a configuration with less than 5% performance loss for 80% applications.
dc.format.mimetypeapplication/pdf
dc.identifier.citationLiu, Ruiqi. "Performance Analysis and Configuration Selection for Applications in the Cloud." (2015) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/88100">https://hdl.handle.net/1911/88100</a>.
dc.identifier.urihttps://hdl.handle.net/1911/88100
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectCloud Computing
dc.subjectPerformance Analysis
dc.subjectConfiguration Selection
dc.titlePerformance Analysis and Configuration Selection for Applications in the Cloud
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputer Science
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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