| KEYWORD |
Scheduling Tasks in Edge-Cloud Continuum with AI/ML
Parole chiave KUBERNETES, MACHINE LEARNING
Riferimenti GUIDO MARCHETTO, ALESSIO SACCO
Gruppi di ricerca DAUIN - GR-03 - COMPUTER NETWORKS GROUP - NETGROUP
Descrizione Task scheduling involves the strategic allocation of incoming workloads to the available machines within a cluster, constituting a critical decision-making process. This function plays a pivotal role in optimizing resource allocation within Edge and Cloud continuum environments. Numerous scheduling challenges have been explored in research, with the goal of either minimizing or maximizing specific objectives through the mapping of tasks onto available machines. In this thesis the student has the opportunity to study a new algorithm to efficiently schedule tasks in the continuuum. This algorithm will use novel AI/ML algorithms, in particular one class of algorithms that can be used is Reinforcement Learning (RL).
Scadenza validita proposta 13/02/2025
PROPONI LA TUA CANDIDATURA