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dc.contributor.authorZhang, Junpeng
dc.date.accessioned2021-12-09T06:33:57Z
dc.date.available2021-12-09T06:33:57Z
dc.date.issued2021-12-09
dc.identifier.urihttp://hdl.handle.net/2077/70249
dc.description.abstractFunctions as a Service (FaaS) has become a trend in software engineering due to its simplicity, elasticity, and cost-effectiveness. FaaS has drawn both the industry’s and researchers’/practitioners’ attention. We notice that more applications are shifted to cloud platforms; however few studies are conducted on how to deploy a FaaS application in a cost-efficiency way. From the perspective of deploying a FaaS application, resource allocation optimization and application-level latency reduction are the two factors that affect the overall performance and total running cost of a FaaS application. Currently, many developers manually analyze the execution logs and run multiple trials to predict a proper deployment strategy or just deploy functions with the finest granularity by default. Such tasks require a considerable amount of human effort, and it has to be done repeatedly whenever the FaaS platform carries out performance-related upgrading. To mitigate this problem, we explore several potential solutions and implement a highly automated framework, which can optimize the deployment of an application from both the perspectives of memory allocation and application-level latency reduction. This study has been conducted by following the guideline of design science research methodology. Afterwards, a controlled experiment is performed to evaluate the framework. The preliminary evaluation reveals that the framework successfully delivers the optimal strategies for cheapest, fastest, and trade-off balanced (on the specific test case, the framework identifies a 10.5% speed gap and 13.3% cost difference between the most optimal case and the worst case). Furthermore, the framework is open-sourced on GitHub for further studies.sv
dc.language.isoengsv
dc.subjectFunction as a Servicesv
dc.subjectFaaSsv
dc.subjectdeployment optimizationsv
dc.subjectmemory allocationsv
dc.subjectfusionsv
dc.subjectlatency reductionsv
dc.titleMulti-level FaaS Application Deployment Optimizationsv
dc.typetext
dc.setspec.uppsokTechnology
dc.type.uppsokH2
dc.contributor.departmentGöteborgs universitet/Institutionen för data- och informationsteknikswe
dc.contributor.departmentUniversity of Gothenburg/Department of Computer Science and Engineeringeng
dc.type.degreeStudent essay


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