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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