Online busy time scheduling
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Liu, Alison | |
| dc.contributor.author | Bovenkamp, Rick van de | |
| dc.date.accessioned | 2024-05-06T23:02:11Z | |
| dc.date.available | 2024-05-06T23:02:11Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/46372 | |
| dc.description.abstract | In this thesis we study the online busy time scheduling problem with infinite processors, where each job has a release time , a processing time and a deadline . The objective of busy time scheduling is to use multiple processors to schedule jobs concurrently in order to minimize the time a machine has to be processing jobs. We present an algorithm using machine learned advice that is able to achieve better results than a pure online algorithm. This algorithm will use a trust parameter, λ ∈ [0, 1], which allows us to control the tradeoff between consistency and robustness. Moreover, for purely online busy time problem, we introduce a lower bound of 2 for eager algorithms, disprove the currently claimed upper bound of 4, and present a general framework for analysis. | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | Online busy time scheduling | |
| dc.title | Online busy time scheduling | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | online algorithms;busy time;energy efficiency;jobs;processors;release time;processing time;deadline;minimization;lower bound;upper bound;connected components;scheduling;machine learning;advice;predictions;trust;consistency;robustness | |
| dc.subject.courseuu | Computing Science | |
| dc.thesis.id | 30639 |
