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dc.contributor.advisorBongo, Lars Ailo
dc.contributor.authorBaig, Mirza Aneeq Hassan
dc.date.accessioned2023-06-14T05:35:14Z
dc.date.available2023-06-14T05:35:14Z
dc.date.issued2023-05-14en
dc.description.abstractThis master’s thesis evaluates the scalability and cost-effectiveness of the AWS cloud platform used to collect and utilize data generated by the 87 digitally equipped trams. The SL-18 Cloud Platform was developed before the trams arrived, and resource configuration estimates were made to handle the data generated by the trams. However, with a few trams currently operational, it is crucial to evaluate the allocation of resources to the services based on actual data. Thus, the thesis's objective is to estimate the data generated by all 87 trams and evaluate the current resource provisioning on the AWS Cloud Platform in terms of scalability and cost. By doing so, this study will provide insights into the optimal resource allocation required for the AWS Cloud Platform to accommodate the data generated by the trams. In this study, we use an existing Digital Twin tool for the trams to evaluate the scalability of the platform, ensuring that it can handle the load while keeping the cost low. To achieve this, the existing Digital Twin is modified to run 87 or more instances concurrently. Using this modified tool, the SL-18 IT platform, which processes real-time data from all 87 trams simultaneously, is evaluated. We monitored the metrics of AWS services to identify any issues. Then based on measurements, we make recommendations for each service's upgrading, downgrading, or keeping the current configuration. Most services are recommended to scale down to reduce costs, while three services require scaling up to be operational. Although our process is well-defined and could be replicated by other studies, it is crucial to have in-depth discussions with the relevant teams for each service and perform repeated validations and evaluations. This is also a necessary protocol in Sporveien to present the results to the various stakeholders and implement the recommended changes. With these changes, Sporveien can save costs and most importantly have a platform capable of handling the data load of 87 SL-18 trams.en_US
dc.identifier.urihttps://hdl.handle.net/10037/29389
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universitetno
dc.publisherUiT The Arctic University of Norwayen
dc.rights.holderCopyright 2023 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDINF-3990
dc.subjectComputer Scienceen_US
dc.subjectCloud Computingen_US
dc.subjecttrams on clouden_US
dc.subjectDigital Twinsen_US
dc.subjectDigital Twin of sensorsen_US
dc.subjectOptimization of Cloud-based Platformen_US
dc.subjectIT infrastructure to collect sensor dataen_US
dc.subjectTram Data Analysisen_US
dc.titleTram-tastic Cloud Computingen_US
dc.typeMastergradsoppgaveno
dc.typeMaster thesisen


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)