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20222026
most citedCarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency

71 citations · 116 across the 16 of their papers we have counts for

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cs.DC2026

The Internet of Collaborating Things: Agentic Edge AI for Autonomous Cross-Domain Collaboration

Walid A. Hanafy, Nader Sehatbakhsh, David Irwin +2

The Internet of Things is on a trajectory toward a trillion connected devices deployed across multiple domains. These devices are no longer simple sensing and actuation endpoints;…

cs.DC2026

FMplex: Model Virtualization for Serving Extensible Foundation Models

Hetvi Shastri, Pragya Sharma, Walid A. Hanafy +3

Foundation models (FMs) are increasingly used as backbones for downstream tasks across language, vision, time-series, and multimodal applications. Yet existing model-serving system…

cs.DC2026

Collaborative Processing for Multi-Tenant Inference on Memory-Constrained Edge TPUs

Nathan Ng, Walid A. Hanafy, Prashanthi Kadambi +5

IoT applications increasingly rely on on-device AI accelerators to ensure high performance, especially in low-connectivity and safety-critical scenarios. However, the limited on-ch…

cs.DC2025

Quantifying the Carbon Reduction of DAG Workloads: A Job Shop Scheduling Perspective

Roozbeh Bostandoost, Adam Lechowicz, Walid A. Hanafy +2

Carbon-aware schedulers aim to reduce the operational carbon footprint of data centers by running flexible workloads during periods of low carbon intensity. Most schedulers treat w…

cs.DC2025

CarbonFlex: Enabling Carbon-aware Provisioning and Scheduling for Cloud Clusters

Walid A. Hanafy, Li Wu, David Irwin +1

Accelerating computing demand, largely from AI applications, has led to concerns about its carbon footprint. Fortunately, a significant fraction of computing demand comes from batc…

cs.DC2025

FailLite: Failure-Resilient Model Serving for Resource-Constrained Edge Environments

Li Wu, Walid A. Hanafy, Tarek Abdelzaher +3

Model serving systems have become popular for deploying deep learning models for various latency-sensitive inference tasks. While traditional replication-based methods have been us…