Abstract

The carrier sense multiple access (CSMA) algorithm has been used in the wireless medium access control (MAC) under standard 802.11 implementation due to its simplicity and generality. An extensive body of research on CSMA has long been made not only in the context of practical protocols, but also in a distributed way of optimal MAC scheduling. However, the current state-of-the-art CSMA (or its extensions) still suffers from poor performance, especially in multi-hop scenarios, and often requires patch-based solutions rather than a universal solution. In this paper, we propose an algorithm which adopts an experience-driven approach and train CSMA-based wireless MAC by using deep reinforcement learning. We name our protocol, Neuro-DCF. Two key challenges are: (i) a stable training method for distributed execution and (ii) a unified training method for embracing various interference patterns and configurations. For (i), we adopt a multi-agent reinforcement learning framework, and for (ii) we introduce a novel graph neural network (GNN) based training structure. We provide extensive simulation results which demonstrate that our protocol, Neuro-DCF, significantly outperforms 802.11 DCF and a O-DCF, recent theory-based MAC protocol, especially in terms of improving delay performance while preserving optimal utility. We believe our multi-agent reinforcement learning based approach would get broad interest from other learning-based network controllers in different layers that require distributed operation.

https://zoom.us/j/5675100945? pwd=MUZUQTRZNldTRmhI MTUwdGlhejBhdz09

Bio

Sangwoo Moon is a Ph.D. Candidate at KAIST under professor Yung Yi. He received a bachelor's degree in Electrical Engineering from KAIST in February 2011 and a master's degree in Electrical Engineering from KAIST in February 2013. His research interests include multi-agent reinforcement learning and wireless networks.

Abstract

Despite recent advances in datacenter networks, timeouts caused by congestion packet losses still remain a major cause of high tail latency. Priority-based Flow Control (PFC) was introduced to make the network lossless, but its Head-of-Line blocking nature causes various performance and management problems. In this paper, we ask if it is possible to design a network that achieves (near) zero timeout only using commodity hardware in datacenters. Our answer is TLT, an extension to existing transport designed to eliminate timeouts. We are inspired by the observation that only certain types of packet drops cause timeouts. Therefore, instead of blindly dropping (TCP) or not dropping packets at all (RoCEv2), TLT proactively drops some packets to ensure the delivery of more important ones, whose losses may cause timeouts. It classifies packets at the host and leverages color-aware thresholding, a feature widely supported by commodity switches, to proactively drop some less important packets. We implement TLT prototypes using VMA to test with real applications. Our testbed evaluation on Redis shows that TLT reduces 99%-ile FCT up to 91.7% on handling bursts of SET operations. In large-scale simulations, TLT augments diverse datacenter transports, from widely-used (TCP, DCTCP, DCQCN) to state-of-the-art (IRN and HPCC), by achieving up to 81% lower tail latency.

https://zoom.us/j/5675100945? pwd=MUZUQTRZNldTRmhI MTUwdGlhejBhdz09

Bio

Hwijoon Lim is a Ph.D. candidate at KAIST under professor Dongsu Han in Intelligent Network Architecture Laboratory. He received a bachelor's degree in Electrical Engineering from KAIST in August 2018. His research interests include data center networking, transport protocols, and cloud computing.