25 citations · 32 across the 8 of their papers we have counts for
6 papers · 1 filter
LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers
Avisek Naug, Antonio Guillen, Vineet Kumar +7
Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock gre…
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters
Soumyendu Sarkar, Avisek Naug, Antonio Guillen +7
Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimiz…
Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi +5
We present a Reinforcement Learning Platform for Adversarial Black-box untargeted and targeted attacks, RLAB, that allows users to select from various distortion filters to create…
SustainDC: Benchmarking for Sustainable Data Center Control
Avisek Naug, Antonio Guillen, Ricardo Luna +8
Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant amounts of energy and contribute to climate ch…
A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration
Avisek Naug, Antonio Guillen, Ricardo Luna Gutierrez +5
There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers. The design and intelligent control for data…
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
Soumyendu Sarkar, Avisek Naug, Ricardo Luna +6
As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporat…