From the 1 of 5 linked papers with an AI index.
5 papers
Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning
Nehal Baganal Krishna, Anam Tahir, Firas Khamis +3
Olaf is a programmable in‑network accelerator that aggregates asynchronous reinforcement‑learning model updates within the data‑plane queue to reduce staleness and congestion, impr…
A Protocol-Independent Transport Architecture
Kimiya Mohammadtaheri, David Gao, Samuel Zhang +8
The network transport layer is increasingly implemented in the NIC hardware to meet the performance demands of modern workloads, but this has made it difficult to evolve or deploy…
Analyzing Symbolic Properties for DRL Agents in Systems and Networking
Mohammad Zangooei, Jannis Weil, Amr Rizk +2
Deep reinforcement learning (DRL) has shown remarkable performance on complex control problems in systems and networking, including adaptive video streaming, wireless resource mana…
A Target-Agnostic Protocol-Independent Interface for the Transport Layer
Pedro Mizuno, Kimiya Mohammadtaheri, Linfan Qian +6
Transport protocols continue to evolve to meet the demands of new applications, workloads, and network environments, yet implementing and evolving transport protocols remains diffi…
Dynamic SLA-aware Network Slice Monitoring
Niloy Saha, Mina Tahmasbi Arashloo, Nashid Shahriar +1
Next-generation networks increasingly rely on network slices - logical networks tailored to specific application requirements, each with distinct Service-Level Agreements (SLAs). E…