Riyad Bensoussane
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Riyad Bensoussane has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Antoine Bernabeu
Nantes Universit, cole Centrale Nantes, LS2N, CNRS, UMR 6004, Nantes, France
1 shared publication - Changgang Zheng
Alibaba Cloud, Hangzhou, China
1 shared publication - Noa Zilberman
University of Oxford, Oxford, United Kingdom
1 shared publication - Shay Vargaftik
VMware Research by Broadcom, Palo Alto, CA, USA
1 shared publication Siim Kaupmees
Department of Computer Science and Technology, University of Cambridge, Cambridge, U.K.
1 shared publicationThanh T. Bui
Department of Computer Science and Technology, University of Cambridge, Cambridge, U.K.
1 shared publication
Latest Publications
- Top Cited Papers
- 16 February 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ACM Transactions on Networking
- Vol. 32 (3) , 2555-2570
IIsy: Hybrid In-Network Classification Using Programmable Switches
The soaring use of machine learning leads to increasing processing demands. As data volume keeps growing, providing classification services with good machine learning performance, high throughput, low latency, and minimal equipment overheads becomes a challenge. Offloading machine learning tasks to network switches can be a scalable solution to this problem, providing high throughput and low latency. However, network devices are resource constrained, and lack support for machine learning functionality. In this paper, we introduce IIsy -a novel mapping tool of machine learning classification models to off-the-shelf switches. Using an efficient encoding algorithm, enables fitting a range of classification models on switches, co-existing with standard switch functionality. To overcome resource constraints, adopts a hybrid approach for ensemble models, running a small model on a switch and a large model on the backend. The evaluation shows that achieves near-optimal classification results, within minimum resource overheads, and while reducing the load on the backend by 70% for data-intensive use cases.
- 16 February 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ACM Transactions on Networking
- Vol. 32 (3) , 2555-2570
Top Cited PapersIIsy: Hybrid In-Network Classification Using Programmable Switches
The soaring use of machine learning leads to increasing processing demands. As data volume keeps growing, providing classification services with good machine learning performance, high throughput, low latency, and minimal equipment overheads becomes a challenge. Offloading machine learning tasks to network switches can be a scalable solution to this problem, providing high throughput and low latency. However, network devices are resource constrained, and lack support for machine learning functionality. In this paper, we introduce IIsy -a novel mapping tool of machine learning classification models to off-the-shelf switches. Using an efficient encoding algorithm, enables fitting a range of classification models on switches, co-existing with standard switch functionality. To overcome resource constraints, adopts a hybrid approach for ensemble models, running a small model on a switch and a large model on the backend. The evaluation shows that achieves near-optimal classification results, within minimum resource overheads, and while reducing the load on the backend by 70% for data-intensive use cases.