25 citations · 50 across the 6 of their papers we have counts for
6 papers
A Tale of Two Scales: Reconciling Horizontal and Vertical Scaling for Inference Serving Systems
Kamran Razavi, Mehran Salmani, Max Mühlhäuser +2
Inference serving is of great importance in deploying machine learning models in real-world applications, ensuring efficient processing and quick responses to inference requests. H…
NetNN: Neural Intrusion Detection System in Programmable Networks
Kamran Razavi, Shayan Davari Fard, George Karlos +3
The rise of deep learning has led to various successful attempts to apply deep neural networks (DNNs) for important networking tasks such as intrusion detection. Yet, running DNNs…
Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling
Kamran Razavi, Saeid Ghafouri, Max Mühlhäuser +2
Mobile and IoT applications increasingly adopt deep learning inference to provide intelligence. Inference requests are typically sent to a cloud infrastructure over a wireless netw…
IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency
Saeid Ghafouri, Kamran Razavi, Mehran Salmani +5
Efficiently optimizing multi-model inference pipelines for fast, accurate, and cost-effective inference is a crucial challenge in machine learning production systems, given their t…
Reconciling High Accuracy, Cost-Efficiency, and Low Latency of Inference Serving Systems
Mehran Salmani, Saeid Ghafouri, Alireza Sanaee +5
The use of machine learning (ML) inference for various applications is growing drastically. ML inference services engage with users directly, requiring fast and accurate responses.…
Operator as a Service: Stateful Serverless Complex Event Processing
Manisha Luthra, Sebastian Hennig, Kamran Razavi +2
Complex Event Processing (CEP) is a powerful paradigm for scalable data management that is employed in many real-world scenarios such as detecting credit card fraud in banks. The s…