On Performance Tuning of Serverless IoT Applications

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dc.contributor.advisorKhazaei, Hamzeh
dc.contributor.authorDantas, Jaime Cristalino Jales
dc.date.accessioned2022-08-08T15:54:05Z
dc.date.available2022-08-08T15:54:05Z
dc.date.copyright2022-04-18
dc.date.issued2022-08-08
dc.date.updated2022-08-08T15:54:05Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractCloud computing has become a predominant IT operation platform in the past decade. Small and large companies have been migrating their workloads to the cloud, and serverless architectures, such as container and Function as a Service (FaaS), are among the popular choices for cluster software deployments. Within this context, autoscaling, the ability to dynamically adapt the cluster capacity based on the current demand is pivotal for maintaining Quality of Service (QoS) and optimizing the cost in the presence of workload fluctuations. The first contribution of this thesis is a novel autoscaling solution that uses burstable instances along with regular instances to handle the queueing arising in traffic and flash crowds. In a second contribution, we evaluate different types of deployments for FaaS, and present three recommendations that developers can consider when deploying their workloads on the public cloud. Finally, we present a resource-aware dynamic load balancer component for edge computing platforms using one of the most fast-growing IoT services in the industry. The contributions are tested and validated on public clouds.
dc.identifier.urihttp://hdl.handle.net/10315/39639
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsCloud computing
dc.subject.keywordsServerless computing
dc.subject.keywordsEdge computing
dc.subject.keywordsAWS Lambda
dc.subject.keywordsAWS Greengrass
dc.subject.keywordsAutoscaling
dc.subject.keywordsFunction as a Service
dc.subject.keywordsInternet of Things
dc.subject.keywordsPerformance of cloud systems
dc.titleOn Performance Tuning of Serverless IoT Applications
dc.typeElectronic Thesis or Dissertation

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