An aI-driven resource-recommendation system for cloud HPC

Liakopoulos, Dimitrios; Λιακόπουλος, Δημήτρης

dc.contributor.author	Liakopoulos, Dimitrios	en
dc.contributor.author	Λιακόπουλος, Δημήτρης	el
dc.date.accessioned	2023-01-23T10:44:45Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/56823
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.24521
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Resource-Recommendation System	en
dc.subject	High-Performance Computing	en
dc.subject	Neural Networks	en
dc.subject	Cloud Computing	en
dc.subject	Hardware Acceleration	en
dc.subject	Σύστημα Σύστασης-Πόρων	el
dc.subject	Υπολογισμός Υψηλής-Επίδοσης	el
dc.subject	Νευρωνικά Δίκτυα	el
dc.subject	Yπολογιστικό Nέφος	el
dc.subject	Επιτάχυνση Υλικού	el
dc.title	An aI-driven resource-recommendation system for cloud HPC	en
heal.type	bachelorThesis
heal.classification	Microcomputers and Digital Systems	en
heal.dateAvailable	2024-01-22T22:00:00Z
heal.language	el
heal.language	en
heal.access	embargo
heal.recordProvider	ntua	el
heal.publicationDate	2022-10-27
heal.abstract	In recent years, the Cloud has evolved into a powerful environment with many instances available for the user to choose. Especially, for High-Performance Computing (HPC) applications the vast amount of available hardware leaves the user with a challenging choice to make. When making this choice, many aspects should be taken into account, in particular the available budget and the time-frame of the project. This information, although fundamental, is really hard to acquire before project completion. In this thesis, a Resource-Recommendation System, based on Neural-Networks (NNs), is proposed in order to help the user determine which instance fits their needs best and help them make their choice. For these reasons, different Multi-Layer Perceptrons (MPLs) where selected and evaluated on different High-Performance Computing (HPC) benchmarks, both synthetic and application. Initially, raw-data was collected from different Cloud instances for each benchmark and organised in a proper way to later be used. The next phase, was the data-prepossessing, in which each data-set was surveyed in order to determine the available trends and a synthetically-generated, more complete data-set was created for the majority of the benchmarks. Lastly, the new syntheticallygenerated data-set was encoded in a proper way and it was fed into the Neural Network (NN) in order to predict the execution times of each benchmark. In total, a combination of six different benchmarks and five Multi-Layer Perceptros (MLPs) were trained and evaluated. Last but not least, the training of the Neural-Networks (NNs) was realised both on Graphics Processing Units (GPUs) and Graphcore’s Intelligence Processing Units (IPUs).	en
heal.advisorName	Soudris, Dimitrios	en
heal.committeeMemberName	Soudris, Dimitrios	en
heal.committeeMemberName	Tsanakas, Panayiotis	en
heal.committeeMemberName	Xydis, Sotirios	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών. Εργαστήριο Μικροϋπολογιστών και Ψηφιακών Συστημάτων VLSI	el
heal.academicPublisherID	ntua
heal.numberOfPages	149 σ.	el
heal.fullTextAvailability	false