PhD Defense: Muhammad Ali

The PhD defense, entitled “Coded Data Storage and Job Placement for Energy-Constrained Data Centers” will take place on Monday, December 4th, 2023, at 2:00 PM (Paris Time) in the Curium at ENSEA (6 Av. du Ponceau, 95000 Cergy, France).

The jury is composed of:
Reviewer: Pr. Michela Meo, Politecnico di Torino, Italy
Reviewer: Pr. Kave Salamatian, University Savoie Mont Blanc, France
Examiner: Pr. Veronica Belmega, University Gustave Eiffel, France
Examiner: Pr. Alexandre Graell I Amat, Chalmers University of Technology, Sweden
Invited: Dr. Jerome David, CEO, Zero Energy Network Tech. (ZenT), France
Supervisor. Pr. Iryna Andriyanova, CY Cergy Paris University, France

For those who want to attend remotely, please use the link below. It will be open from 1:30 PM.

https://cnrs.zoom.us/j/99497093589?pwd=N3E3MWxHbUJrejlwc29RdFNqUXlsdz09

Meeting ID: 994 9709 3589
Passcode: a5VAg8

Thesis Title: Coded Data Storage and Job Placement for Energy-Constrained Data Centers

Abstract: Data centers are accountable for a substantial portion of global energy consumption. This consumption is anticipated to rise in the coming years, driven by the increasing demand for data center services. Therefore, the need for energy-efficient, low-carbon data center operations is growing rapidly. This research focuses on designing and implementing a low-carbon, energy-efficient data center powered by solar and hydrogen, granting it independence from the power grid. As a result, the data center is limited by the upper bound on the energy consumption, which is 10KWh. The maximum usage of energy-constraint imposes several challenges to the design, energy usage, and sustainability of the data center.

The work first contributes to designing a low-power budget data center while respecting the overall energy constraint. We tried to save the energy usage of the data center through the right choice of hardware while keeping the performance of the data center intact. The second contribution of our work provides valuable protocols like lazy repair in distributed data storage, job placement, and power management techniques to further reduce the data center’s energy usage. With the combined efforts of the right choice of hardware, protocols, and techniques, we significantly reduced the overall energy consumption of the data center.