| KEYWORD |
Area Engineering
Scheduling Tasks in Edge-Cloud Continuum with AI/ML
keywords KUBERNETES, MACHINE LEARNING
Reference persons GUIDO MARCHETTO, ALESSIO SACCO
Research Groups DAUIN - GR-03 - COMPUTER NETWORKS GROUP - NETGROUP
Description 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).
Deadline 13/02/2025
PROPONI LA TUA CANDIDATURA