71 citations · 94 across the 5 of their papers we have counts for
5 papers
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…
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…
AI on the Edge: Rethinking AI-based IoT Applications Using Specialized Edge Architectures
Qianlin Liang, Prashant Shenoy, David Irwin
Edge computing has emerged as a popular paradigm for supporting mobile and IoT applications with low latency or high bandwidth needs. The attractiveness of edge computing has been…