Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.768698
Title: Evaluation of cloud computing modelling tools : simulators and predictive models
Author: Alshammari, Dhahi
ISNI:       0000 0004 7655 0236
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 2018
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Abstract:
Experimenting with novel algorithms and configurations for the automatic management of Cloud Computing infrastructures is expensive and time consuming on real systems. Cloud computing delivers the benefits of using virtualisation techniques to data centers instead of physical servers for customers. However, it is still complex for researchers to test and run their experiments on data center due to the cost for repeating the experiments. To address this, various tools are available to enable simulators, emulators, mathematical models, statistical models and benchmarking. Despite this, there are different methods used by researchers to avoid the difficulty of conducting Cloud Computing research on actual large data centre infrastructure. However, it is still difficult to chose the best tool to evaluate the proposed research. This research focuses on investigating the level of accuracy of existing known simulators in the field of cloud computing. Simulation tools are generally developed for particular experiments, so there is little assurance that using them with different workloads will be reliable. Moreover, a predictive model based on a data set from a realistic data center is delivered as an alternative model of simulators as there is a lack of their sufficient accuracy. So, this work addresses the problem of investigating the accuracy of different modelling tools by developing and validating a procedure based on the performance of a target micro data centre. Key insights and contributions are: Involving three alternative models for Cloud Computing real infrastructure showing the level of accuracy of selected simulation tools. Developing and validating a predictive model based on a Raspberry Pi small scale data centre. The use of predictive model based on Linear Regression and Artificial Neural Net- works models based on training data set drawn from a Raspberry Pi Cloud infrastructure provides better accuracy.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.768698  DOI: Not available
Keywords: Q Science (General)
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