45 citations · 214 across the 86 of their papers we have counts for
12 papers · 1 filter
Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for Sustainable Data Center Operation
Jason Crop, Hayden Moore, Sudeep Pasricha
As data center energy demand approaches grid-level constraints, optimizing conventional server infrastructure is essential for sustainable growth. The long-standing assumption that…
MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenters
H. Moore, S. Qi, D. Milojicic +2
Large Language Models (LLMs) have become increasingly prevalent in cloud-based platforms, propelled by the introduction of AI-based consumer and enterprise services. LLM inference…
Sustainable Graph Analytics Workload Scheduling with Evolutionary Reinforcement Learning in Edge-Cloud Systems
P. Ramicetty, H. Moore, S. Qi +5
Graph analytics powers modern intelligent systems such as smart cities, cyber-physical infrastructure, IoT security, and large-scale social networks. As these workloads scale in co…
Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenters
Hayden Moore, Sirui Qi, Ninad Hogade +3
In recent years, Large Language Models (LLM) such as ChatGPT, CoPilot, and Gemini have been widely adopted in different areas. As the use of LLMs continues to grow, many efforts ha…
A Framework for SLO, Carbon, and Wastewater-Aware Sustainable FaaS Cloud Platform Management
Sirui Qi, Hayden Moore, Ninad Hogade +3
Function-as-a-Service (FaaS) is a growing cloud computing paradigm that is expected to reduce the user cost of service over traditional serverful approaches. However, the environme…
CASA: A Framework for SLO and Carbon-Aware Autoscaling and Scheduling in Serverless Cloud Computing
S. Qi, H. Moore, N. Hogade +3
Serverless computing is an emerging cloud computing paradigm that can reduce costs for cloud providers and their customers. However, serverless cloud platforms have stringent perfo…
Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
Ninad Hogade, Sudeep Pasricha
Data centers are increasingly using more energy due to the rise in Artificial Intelligence (AI) workloads, which negatively impacts the environment and raises operational costs. Re…
MOSAIC: A Multi-Objective Optimization Framework for Sustainable Datacenter Management
Sirui Qi, Dejan Milojicic, Cullen Bash +1
In recent years, cloud service providers have been building and hosting datacenters across multiple geographical locations to provide robust services. However, the geographical dis…
SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management
Sirui Qi, Dejan Milojicic, Cullen Bash +1
Today's cloud data centers are often distributed geographically to provide robust data services. But these geo-distributed data centers (GDDCs) have a significant associated enviro…
A Reinforcement Learning Framework with Region-Awareness and Shared Path Experience for Efficient Routing in Networks-on-Chip
Kamil Khan, Sudeep Pasricha
Network-on-chip (NoC) architectures provide a scalable, high-performance, and reliable interconnect for emerging manycore systems. The routing policies used in NoCs have a signific…
A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management
Ninad Hogade, Sudeep Pasricha
Cloud workloads today are typically managed in a distributed environment and processed across geographically distributed data centers. Cloud service providers have been distributin…
Energy and Network Aware Workload Management for Geographically Distributed Data Centers
Ninad Hogade, Sudeep Pasricha, Howard Jay Siegel
Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution. With increasing data volumes in emerging cl…