Interference-aware classification and deployment of database workloads on heterogeneous memory systems

Δούλου, Νίκη; Doulou, Niki

dc.contributor.author	Δούλου, Νίκη	el
dc.contributor.author	Doulou, Niki	en
dc.date.accessioned	2025-10-06T11:37:46Z
dc.date.available	2025-10-06T11:37:46Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/62651
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.30347
dc.rights	Default License
dc.subject	Heterogeneous Memory Systems	en
dc.subject	Database Workloads	en
dc.subject	Interference aware classification	en
dc.subject	Ετερογενή Συστήματα Μνήμης	el
dc.subject	Βάσεις δεδομένων	el
dc.subject	Ταξινόμηση με επίγνωση παρεμβολών	el
dc.title	Interference-aware classification and deployment of database workloads on heterogeneous memory systems	en
dc.title	Ταξινόμηση με επίγνωση παρεμβολών και εκτέλεση εφαρμογών βάσεων δεδομένων σε ετερογενές σύστημα μνήμης	el
heal.type	bachelorThesis
heal.classification	Σχεδιασμός Ενσωματωμένων Συστημάτων	el
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2025-03-14
heal.abstract	Over the recent years, the rapid growth of applications has led to a corresponding increase in the computational requirements and the volume of data. At the same time, traditional memory technologies based on DRAM technologies have reached their physical limits in terms of scalability and sustainability. As a result, a novel memory technology architecture has been introduced, based on heterogeneous memory systems, that integrate DRAM and NVM technologies. However, some several challenges arise regarding application placement and their management. In this work, it is conducted an extensive profiling analysis and characterization of database applications. The experimental analysis is performed both in an isolated computing system environment and in a interference-aware system using specific benchmarks that stress the resources of the system. Additionally, machine learning models are trained based on the data collected during the profiling analysis, and applications are classified into memory-bound, cpu-bound or bandwidth-bound depending on their performance. The models achieve prediction accuracy over 90%. Finally, conclusions can be drawn regarding the placement of applications in the heterogeneous system.	en
heal.advisorName	Σούντρης, Δημήτριος	el
heal.committeeMemberName	Ξύδης, Σωτήριος	el
heal.committeeMemberName	Λεντάρης, Γεώργιος	el
heal.committeeMemberName	Σούντρης, Δημήτριος	el
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	89 σ.	el
heal.fullTextAvailability	false