Innovative scheduling approaches for Cloud-HPC heterogeneous environments
Reference persons BARTOLOMEO MONTRUCCHIO
Description The pace at which Cloud-based services are adopted is pushing Cloud service providers (CSPs) to adopt more heterogeneous resources in their data centres. Computing resource diversification is pushed also by the growing adoption of machine learning algorithms (also HPC applications are going in this direction), which generally require specialized hardware to efficiently execute. Managing resources and tasks (i.e., deciding an allocation of the tasks under a certain set of constraints) at a large scale requires innovative scheduling techniques. However, current schedulers still rely on simple strategies to assign tasks to available resources.
The objective of the work is to study innovative approaches for managing resources in Cloud-HPC environments, in the context of the ACROSS EuroHPC project (https://www.acrossproject.eu). To this end, machine learning and optimization techniques will be considered for improving the quality of task scheduling when heterogeneous resources are available (e.g., GPUs, FPGAs, dedicated ASICs). Studied techniques will look at improving scheduling under different constraints (energy saving, reducing task makespan, etc.).
The pre-requirements to successfully address this thesis are experience with main programming languages (Python, C/C++, Rust) and knowledge about optimization algorithms.
The duration of the thesis work is expected to be around 6 months, adjustable based on the specific needs and skills.
Send CV to email@example.com and firstname.lastname@example.org specifying the thesis title.
Deadline 19/12/2023 PROPONI LA TUA CANDIDATURA