This study aims to explore predictive scaling algorithms used to predict and manage workloadsin a containerized system. The goal is to identify which predictive scaling approach delivers themost effective results, contributing to research on cloud elasticity and resource management.This potentially leads to reduced infrastructure costs while maintaining efficient performance,enabling a more sustainable cloud-computing technology. The work involved the developmentand comparison of three different autoscaling algorithms with an interchangeable predictioncomponent. For the predictive part, three different time-series analysis methods were used:XGBoost, ARIMA, and Prophet. A simulation system with the necessary modules wasdeveloped, as well as a designated target service to experience the load. Each algorithm'sscaling accuracy was evaluated by comparing its suggested number of instances to the optimalnumber, with each instance representing a simulated CPU core. The results showed varyingefficiency: XGBoost and Prophet excelled with richer datasets, while ARIMA performed betterwith limited data. Although XGBoost and Prophet maintained 100% uptime, this could lead toresource wastage, whereas ARIMA's lower uptime percentage possibly suggested a moreresource-efficient, though less reliable, approach. Further analysis, particularly experimentalinvestigation is required to deepen the understanding of these predictors' influence on resourceallocation.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-531329 |
| Date | January 2024 |
| Creators | Dahl, Johanna, Strömbäck, Elsa |
| Publisher | Uppsala universitet, Datalogi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC STS, 1650-8319 ; 24025 |
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