Date of Award

2021

Document Type

Thesis

Degree Name

Master of Science in Engineering (M.S.Eng.)

Department

Engineering

First Advisor

Venkittaraman Krishnamani Pallipuram

First Committee Member

David Mueller

Second Committee Member

Jinzhu Gao

Abstract

Cloud computing has greatly impacted the scientific community and the end users. By leveraging cloud computing, small research institutions and undergraduate colleges are able to alleviate costs and achieve research goals without purchasing and maintaining all the hardware and software. In addition, cloud computing allows researchers to access resources as their teams require and allows real-time collaboration with team members across the globe. Nowadays however, users are easily overwhelmed by the wide range of cloud servers and instances. Due to differences between the cloud server platforms and between instances within the platform, users find it difficult to identify the right instance match for their application.

Therefore, we propose the A2Cloud-Hierarchy (A2Cloud-H) framework that recommends Cloud instances to users for high-performance scientific computing. The framework comprises four components: training data collection, supervised learning (SL) module, unsupervised learning (USL) module, and a decision module. The training database comprise testing traces of previous application and Cloud instances; these are contributed by the scientific community. The SL module contains three popular supervised learning modules: logistic regression, support vector machine and random forest, which train using the database to qualitatively assess the instance performance for the target application. The USL module includes three collaborative filtering methods: application-based, instance-based and rank-based, which use the database to estimate the instances’ performance ratings for the target application. The decision module comprises multiple tiers of analytic hierarchy processing, which consolidate the instance recommendation from the SL and USL modules into a final instance recommendation.

The model is trained and validated by 8 real-world applications on 20 Cloud instances, yielding more than 90% modeling accuracy. The recommendation and integration method proposed in this thesis can help promote a better cloud computing environment for both end-users and cloud server platforms.

Pages

70

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