Deep Learning techniques for system optimization in Cloud architectures

Μασούρος, Δημοσθένης; Masouros, Dimosthenis

dc.contributor.author	Μασούρος, Δημοσθένης	el
dc.contributor.author	Masouros, Dimosthenis	en
dc.date.accessioned	2023-09-19T09:09:31Z
dc.date.available	2023-09-19T09:09:31Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/58059
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.25756
dc.rights	Default License
dc.subject	Cloud Computing	en
dc.subject	Machine Learning	en
dc.subject	Deep Learning	en
dc.subject	Resource Management	en
dc.subject	Cloud Monitoring	en
dc.subject	Υπολογιστικό Νέφος	el
dc.subject	Βαθιά Μάθηση	el
dc.subject	Μηχανική Μάθηση	el
dc.subject	Οργάνωση Υπολογιστικών Πόρων	el
dc.subject	Σύστημα Παρακολούθησης Υπολογιστικού Νέφους	el
dc.title	Deep Learning techniques for system optimization in Cloud architectures	en
dc.title	Τεχνικές Βαθιάς Μάθησης για Βελτιστοποίηση Συστημάτων Υπολογιστικού Νέφους	el
dc.contributor.department	Εργαστήριο Μικροϋπολογιστών και Ψηφιακών Συστημάτων	el
heal.type	doctoralThesis
heal.classification	Computer engineering	en
heal.classification	Μηχανική υπολογιστών	el
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-05-02
heal.abstract	Nowadays, Cloud computing is becoming one of the most attractive solutions for application execution, due to the enhanced flexibility and efficiency it offers. Cloud computing refers to the on-demand provision of system resources, especially computing power and storage. These resources typically exist on individual servers found in datacenter environments and/or server farms around the world. Proper and efficient management of those resources becomes crucial, both from the providers' as well as end-users' point of view, since it can dramatically improve performance and reduce cost expenses for all the parties involved. However, orchestrating cloud computing resources is not a straightforward matter, due to i) the huge amount of available optimization knobs, ii) the different levels that these optimizations can be applied (server- to cluster- to application-level) and iii) the interrelationship between software and hardware optimizations combined with the extreme hardware heterogeneity found in today's data-centers environments. On top of that novel computing paradigms are emerging (e.g., hardware disaggregation), which unveil extra optimization knobs on the foreground, thus, further complicating the problem of efficient resource orchestration. In this dissertation, we examine the applicability of deep learning techniques for system optimization in Cloud architectures. Given the huge amount and the multi-level nature of the available optimization knobs, which tend to be unmanageable using conventional, human-driven orchestration mechanisms, deep learning approaches appear to be unavoidable in order to exploit and leverage the full potential of modern Cloud systems. First, we investigate the employment of state-of-the-art neural networks in the field of system monitoring, which forms an integral part of modern Cloud infrastructures. Then, we examine the application of ML-driven orchestration on different levels of the hierarchy, i.e., application-driven automatic optimizations, cluster-level application deployment and orchestration and system-level control of running applications. To achieve the above, we propose three frameworks, i.e., Rusty, Adrias and Sparkle, which employ deep learning driven optimizations on different levels of the hierarchy.	en
heal.advisorName	Σούντρης, Δημήτριος	el
heal.advisorName	Soudris, Dimitrios	en
heal.committeeMemberName	Pekmestzi, Kiamal
heal.committeeMemberName	Huebner, Michael
heal.committeeMemberName	Xydis, Sotirios
heal.committeeMemberName	Goumas, Georgios
heal.committeeMemberName	Silvano, Cristina
heal.committeeMemberName	Pnevmatikatos, Dionisios
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	323
heal.fullTextAvailability	false