71 citations · 116 across the 16 of their papers we have counts for
17 papers
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;…
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…
FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models
Kang Yang, Walid A. Hanafy, Prashant Shenoy +1
As edge AI deployments scale to billions of devices running always-on, real-time compound AI pipelines, they represent a massive and largely unmanaged source of energy consumption…
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…
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…
FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines
Hetvi Shastri, Pragya Sharma, Walid A. Hanafy +2
Foundation models (FMs) have opened new avenues for machine learning applications due to their ability to adapt to new and unseen tasks with minimal or no further training. Time-se…
LLM-Driven Auto Configuration for Transient IoT Device Collaboration
Hetvi Shastri, Walid A. Hanafy, Li Wu +3
Today's Internet of Things (IoT) has evolved from simple sensing and actuation devices to those with embedded processing and intelligent services, enabling rich collaborations betw…
MEL: Multi-level Ensemble Learning for Resource-Constrained Environments
Krishna Praneet Gudipaty, Walid A. Hanafy, Kaan Ozkara +4
AI inference at the edge is becoming increasingly common for low-latency services. However, edge environments are power- and resource-constrained, and susceptible to failures. Conv…
Flex: Grid-Responsive Provisioning and Scheduling for Elastic Cloud Clusters
Walid A. Hanafy, Li Wu, David Irwin +1
Computing energy demand and grid capacity growth are at a crossroads. Data centers are becoming large, concentrated electricity consumers just as power grids face capacity shortage…
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…
CarbonEdge: Leveraging Mesoscale Spatial Carbon-Intensity Variations for Low Carbon Edge Computing
Li Wu, Walid A. Hanafy, Abel Souza +5
The proliferation of latency-critical and compute-intensive edge applications is driving increases in computing demand and carbon emissions at the edge. To better understand carbon…
LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand
Roozbeh Bostandoost, Adam Lechowicz, Walid A. Hanafy +3
Motivated by an imperative to reduce the carbon emissions of cloud data centers, this paper studies the online carbon-aware resource scaling problem with unknown job lengths (OCSU)…
The War of the Efficiencies: Understanding the Tension between Carbon and Energy Optimization
Walid A. Hanafy, Roozbeh Bostandoost, Noman Bashir +3
Major innovations in computing have been driven by scaling up computing infrastructure, while aggressively optimizing operating costs. The result is a network of worldwide datacent…
Understanding the Benefits of Hardware-Accelerated Communication in Model-Serving Applications
Walid A. Hanafy, Limin Wang, Hyunseok Chang +3
It is commonly assumed that the end-to-end networking performance of edge offloading is purely dictated by that of the network connectivity between end devices and edge computing f…
CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency
Walid A. Hanafy, Qianlin Liang, Noman Bashir +2
Cloud platforms are increasing their emphasis on sustainability and reducing their operational carbon footprint. A common approach for reducing carbon emissions is to exploit the t…
Ecovisor: A Virtual Energy System for Carbon-Efficient Applications
Abel Souza, Noman Bashir, Jorge Murillo +4
Cloud platforms' rapid growth is raising significant concerns about their carbon emissions. To reduce emissions, future cloud platforms will need to increase their reliance on rene…
Model-driven Cluster Resource Management for AI Workloads in Edge Clouds
Qianlin Liang, Walid A. Hanafy, Ahmed Ali-Eldin +1
Since emerging edge applications such as Internet of Things (IoT) analytics and augmented reality have tight latency constraints, hardware AI accelerators have been recently propos…