This project focuses on adaptive resource management for cloud and edge computing platforms. Our work emphasizes model-driven as well as systems approaches for edge and cloud computing. Our work focuses on the following topics
- Serverless computing resource management
- Edge AI resource management
- Analytic modeling of large distributed systems
- Elastic scaling and resource management
- Power-performance tradeoffs
- Resource management for Internet-scale content delivery networks
Representative Publications
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TailClipper: Reducing Tail Response Time of Distributed Services Through System-Wide Scheduling
Nathan Ng, Abel Souza, Ahmed Ali-Eldin, David Irwin, Don Towsley, and Prashant Shenoy.
In ACM Symposium on Cloud Computing (SoCC), 2024.
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Enhancing Resilience in Distributed ML Inference Pipelines for Edge Computing
Li Wu, Walid A. Hanafy, Abel Souza, Tarek Abdelzaher, Gunjan Verma, and Prashant Shenoy.
In Proceedings of the 44th IEEE Military Communications Conference (MILCOM) workshop on Internet of Things for Adversarial Environments, 2024.
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Dělen: Enabling Flexible and Adaptive Model-serving for Multi-tenant Edge AI
Qianlin Liang, Walid A. Hanafy, Noman Bashir, Ahmed Ali-Eldin, David Irwin, and Prashant Shenoy.
In Proceedings of IEEE/ACM Eighth International Conference on Internet-of-Things Design and Implementation (IoTDI), San Antonio, May 2023.
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The Hidden Cost of the Edge: A Performance Comparison of Edge and Cloud Latencies
Ahmed Ali-Eldin, Bin Wang, and Prashant Shenoy.
In Proceedings of ACM Conference on High Performance Computing, Networking, Storage and Analysis (SC21), St. Louis. MO, USA, Nov 2021. arXiv -
Hedge Your Bets: Optimizing Long-term Cloud Costs by Mixing VM Purchasing Options
Pradeep Ambati, Noman Bashir, David Irwin, Mohammad Hajiesmaili, and Prashant Shenoy.
In Proceedings of IEEE International Conference on Cloud Engineering (IC2E) (pp. 105-115), Sydney, April 2020.
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