Horizontal scalability in cloud environments orchestrated by Kubernetes

Τζαβάρα, Νεφέλη Παναγιώτα; Tzavara, Nefeli Panagiota

dc.contributor.author	Τζαβάρα, Νεφέλη Παναγιώτα	el
dc.contributor.author	Tzavara, Nefeli Panagiota	en
dc.date.accessioned	2024-05-13T09:16:55Z
dc.date.available	2024-05-13T09:16:55Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/59333
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.27029
dc.rights	Default License
dc.subject	Βαθιά Μάθηση	el
dc.subject	Deep Learning	en
dc.subject	Kubernetes	en
dc.subject	Deep Q Learning	en
dc.subject	Reinforcement Learning	en
dc.subject	LSTM	en
dc.subject	Οριζόντια κλιμακωσιμότητα	el
dc.subject	Πρόβλεψη χρονοσειράς	el
dc.subject	Horizontal Scaling	en
dc.subject	Time series forecasting	en
dc.title	Horizontal scalability in cloud environments orchestrated by Kubernetes	en
heal.type	bachelorThesis
heal.classification	Electrical and Computer Engineering	en
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-11-02
heal.abstract	Taking a look into the 21st century’s distinctive features, it is prevalent that the volume of data produced is constantly rising, more than ever. Recent technological innovations in computer science and services have now made possible the remote computation of large data in distributed systems and/or cloud services. The remote computation in these frameworks is made possible by the use of microservices that can run independently in containers who have also become very popular. The organising, or more commonly, the "orchestration" of containers is done by the so called orchestrators, the most popular one being Kubernetes, which is an open-source software. Kubernetes is responsible for scheduling pods in the different nodes of a cluster and also for making decisions about the scale of the cluster. Pods are units that encapsulate collections of containers and are excetuted inside nodes. The scale of the cluster plays a crucial role, as the computational needs of different services are variant and the cluster must be able to withstand the different usage scenarios (e.g. large traffic in a server) . Thus, efficient scaling lies in making efficient and timely predictions about how many and which resources will be allocated to certain processes, so that system failure is avoided and idleness of the resources is minimized. In this thesis, we begin by studying the Kubernetes architecture and the different scaling concepts with a focus on horizontal scaling, meaning the scaling where the number cluster nodes is modified. For our tests, we study the CPU usage in a cluster orchestrated by Kubernetes. Namely, we study anonymized data collected over 15 days from a production server in CERN that contain CPU usage data over time. We perform exploratory data analysis to further understand the data and proceed to develop forecasting models for CPU usage predictions. At last, we develop a reinforcement learning agent with a policy of minimizing CPU usage based on horizontal scaling of the cluster. Our ultimate goal is to propose a proactive custom resource allocator that can be integrated into a Kubernetes cluster, so that the scaling decisions are based on CPU usage prediction and thus a provision of the necessary number of nodes.	en
heal.advisorName	Koziris, Nectarios	en
heal.committeeMemberName	Tsoumakos, Dimitrios	en
heal.committeeMemberName	Konstantinou, Ioannis	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	71 σ.	el
heal.fullTextAvailability	false