Auto-Scaling Containerized Microservice Applications

MirzaEbrahim Mostofi, Vahid

Auto-Scaling Containerized Microservice Applications

dc.contributor.advisor	Krishnamurthy, Diwakar
dc.contributor.advisor	Arlitt, Martin
dc.contributor.author	MirzaEbrahim Mostofi, Vahid
dc.contributor.committeemember	Drew, Steve
dc.contributor.committeemember	Medeiros de Souza, Roberto
dc.date	2021-11
dc.date.accessioned	2021-09-17T17:58:42Z
dc.date.available	2021-09-17T17:58:42Z
dc.date.issued	2021-09
dc.description.abstract	The microservices architecture is being increasingly used to build complex applications. Many such applications are customer-facing. Hence, they face workload fluctuations and need to respond to end user requests quickly in spite of such fluctuations. Furthermore, application owners typically prefer to allocate resources efficiently using containerization technology so that operational costs are kept low. These two requirements are typically implemented within an auto-scaler module. The third requirement, unique to microservice applications, is the need for owners to roll out updates in an agile and frequent manner. Hence, an auto-scaler appropriate for microservices should be designed to support this requirement. Unfortunately, current auto-scaling techniques do not satisfy these three key requirements simultaneously. I develop a novel auto-scaler called TRIM that addresses this open issue. TRIM exploits properties of real-life microservice workloads. Specifically, my analysis of a large dataset consisting of $24,000$ production microservice applications reveals a novel insight that a small number of workload patterns are encountered frequently over any given time period. Furthermore, these popular patterns tend to be popular during a subsequent time period. TRIM pre-computes resource allocations for these small number of popular patterns quickly and re-uses these allocations at runtime when appropriate. I develop MOAT, a novel heuristic optimization technique that ensures that the pre-computed allocations satisfy response time targets efficiently. Using a variety of analytical, on-premise, and public cloud systems, I show that MOAT and TRIM outperform state-of-the-art baselines in terms of all three requirements described previously. For example, I consider an on-premise system subjected to a $24$ hour workload derived from a real-life microservice application. By quickly pre-computing resource allocations for just 5 popular workload patterns in this workload, TRIM achieves up to 70% lesser response time violations and up to 20% reduced costs compared to an industry-standard auto-scaling technique.	en_US
dc.identifier.citation	MirzaEbrahim Mostofi, V. (2021). Auto-scaling containerized microservice applications (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/39214
dc.identifier.uri	http://hdl.handle.net/1880/113888
dc.language.iso	eng	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Software performance	en_US
dc.subject	Kubernetes	en_US
dc.subject	Docker	en_US
dc.subject	Containers	en_US
dc.subject	Auto-scaling	en_US
dc.subject	microservices	en_US
dc.subject.classification	Computer Science	en_US
dc.title	Auto-Scaling Containerized Microservice Applications	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Engineering – Electrical & Computer	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US