Αλγόριθμοι δρομολόγησης με εκτίμηση της χρονικής διάρκειας των εργασιών σε πραγματικό χρόνο

Δραγάζης, Σπυρίδων Κωνσταντίνος; Dragazis, Spyridon Konstantinos

dc.contributor.author	Δραγάζης, Σπυρίδων Κωνσταντίνος	el
dc.contributor.author	Dragazis, Spyridon Konstantinos	en
dc.date.accessioned	2023-01-09T08:43:01Z
dc.date.available	2023-01-09T08:43:01Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/56548
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.24246
dc.rights	Αναφορά Δημιουργού 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/gr/	*
dc.subject	Clairvoyant Scheduling	en
dc.subject	Χρονοδρομολόγηση Αγνώστου Μεγέθους Εργασιών	el
dc.subject	Ανάλυση Δυσκολότερου Στιγμιότυπου	el
dc.subject	Αλγόριθμος Κυκλικής Χρονοδρομολόγησης	el
dc.subject	Αλγόριθμοι Καθοδηγούμενοι από Μηχανική Μάθηση	el
dc.subject	Χρονοδρομολόγηση υπό Αβεβαιότητα	el
dc.subject	Non-Clairvoyant Scheduling	en
dc.subject	Explorable Uncertainty	en
dc.subject	Learning Augmented Algorithms	en
dc.subject	Competitive Analysis	en
dc.subject	Round Robin Algorithm	en
dc.title	Αλγόριθμοι δρομολόγησης με εκτίμηση της χρονικής διάρκειας των εργασιών σε πραγματικό χρόνο	el
heal.type	bachelorThesis
heal.classification	Algorithms	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2022-09-20
heal.abstract	This diploma thesis revisits the problem of Non Clairvoyant Scheduling Under Explorable Un- certainty. The problem we are interested in is to schedule a set of jobs either on a single or on multiple machines. Our goal is to minimize the sum of completion times. In general, Scheduling is one of the most well-studied problems in Computer Science and Operations Research liter- ature with a huge variety of real world applications. In the majority of the scheduling models, the assumption that all characteristics of a job are known in advance, is followed. More analyti- cally, the two main categories in scheduling are divided based on the a priori or not knowledge of the exact characteristics of the jobs. Clairvoyant scheduling studies the case where we are, in advance, aware of the exact features of a job. On the contrary, we talk about non clairvoyant scheduling when there is absence of the characteristics of the jobs until their completion. In prac- tice, in majority of real world applications the exact knowledge of jobs’ features is not possible. In this work, we study the algorithmic aspects that lie in the field of Explorable Uncertainty, which belongs in the intersection of Clairvoyance and Non-Clairvoyance. We start by exploring the well-known Round Robin algorithm for the non-clairvoyant case. Afterwards, we move to a new direction that is Scheduling Under Uncertainty. In this framework, we initially consider an upper bound of the processing characteristics of the jobs and we can dynamically acquire the exact features by paying an extra cost. Finally, we focus on the area of Learning Augmented Algorithms where the goal is to design algorithms that use predictions from Machine Learning Models. In our approach, instead of predictions we decided to use the notion of testing and learn specific information about the jobs in parallel with their execution.	en
heal.advisorName	Φωτάκης, Δημήτριος	el
heal.committeeMemberName	Φωτάκης, Δημήτριος	el
heal.committeeMemberName	Μπάμπης, Ευριπίδης	el
heal.committeeMemberName	Παγουρτζής, Αριστείδης	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	65 σ.	el
heal.fullTextAvailability	false