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Verifiable Sustainability in Data Centers

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Security & Privacy Magazine

Syed Rafiul Hussain Pennsylvania State University, hussain1@psu.edu Patrick McDaniel University of Wisconsin–Madison, mcdaniel@cs.wisc.edu Anshul Gandhi Stony Brook University, anshul@cs.stonybrook.edu Kanad Ghose Binghamton University, ghose@binghamton.edu Kartik Gopalan Binghamton University, kartik@binghamton.edu Dongyoon Lee Stony Brook University, dongyoon@cs.stonybrook.edu Yu David Liu Binghamton University, davidl@binghamton.edu Zhenhua Liu Stony Brook University, zhenhua.liu@stonybrook.edu Shuai Mu Stony Brook University, shuai@cs.stonybrook.edu Erez Zadok Stony Brook University, erez.zadok@stonybrook.edu

(2021)

Abstract

Data centers have significant energy needs, both embodied and operational, affecting sustainability adversely. The current techniques and tools for collecting, aggregating, and reporting verifiable sustainability data are vulnerable to cyberattacks and misuse, requiring new security and privacy-preserv

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Muhammad Shehab

Also published under: Muhammad J. Shehab

Affiliation

Dipartimento di Elettronica, Politecnico di Torino, Turin, Italy


Publication Topics



Biography

Muhammad J. Shehab (Member, IEEE) was born in Ra’s-Beirut, Beirut, Lebanon, in April 1985. He received the Bachelor of Engineering (B.E.) and the Master of Electrical/Communication and Electronics Engineering (M.E.) degrees from Beirut Arab University (BAU), Beirut, in 2007 and 2011, respectively, and the Doctor of Philosophy (Ph.D.) degree in wireless communication from the Department of Electronics and Telecommunications, Politecnico di Torino (POLITO). In 2013, he joined the Department of Information Technology, Community College of Qatar (CCQ), as a Network Engineer and a Lecturer till date. Then, he joined the Faculty of Electrical Engineering, Qatar University (QU), Qatar, in 2018. His research interests include 5G, 6G networks, non-orthogonal multiple access (NOMA), intelligent reflecting surface (IRS), mmWave, THz, massive MIMO, beamforming, machine learning (ML), deep reinforcement learning (DRL),

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