A survey on data center networking (DCN): Infrastructure and operations

W **a, P Zhao, Y Wen, H **e - IEEE communications surveys & …, 2016 - ieeexplore.ieee.org
Data centers (DCs), owing to the exponential growth of Internet services, have emerged as
an irreplaceable and crucial infrastructure to power this ever-growing trend. A DC typically …

Datacenter traffic control: Understanding techniques and tradeoffs

M Noormohammadpour… - … Surveys & Tutorials, 2017 - ieeexplore.ieee.org
Datacenters provide cost-effective and flexible access to scalable compute and storage
resources necessary for today's cloud computing needs. A typical datacenter is made up of …

Swift: Delay is simple and effective for congestion control in the datacenter

G Kumar, N Dukkipati, K Jang, HMG Wassel… - Proceedings of the …, 2020 - dl.acm.org
We report on experiences with Swift congestion control in Google datacenters. Swift targets
an end-to-end delay by using AIMD control, with pacing under extreme congestion. With …

Fast distributed inference serving for large language models

B Wu, Y Zhong, Z Zhang, S Liu, F Liu, Y Sun… - arxiv preprint arxiv …, 2023 - arxiv.org
Large language models (LLMs) power a new generation of interactive AI applications
exemplified by ChatGPT. The interactive nature of these applications demands low latency …

Homa: A receiver-driven low-latency transport protocol using network priorities

B Montazeri, Y Li, M Alizadeh… - Proceedings of the 2018 …, 2018 - dl.acm.org
Homa is a new transport protocol for datacenter networks. It provides exceptionally low
latency, especially for workloads with a high volume of very short messages, and it also …

Re-architecting datacenter networks and stacks for low latency and high performance

M Handley, C Raiciu, A Agache, A Voinescu… - Proceedings of the …, 2017 - dl.acm.org
Modern datacenter networks provide very high capacity via redundant Clos topologies and
low switch latency, but transport protocols rarely deliver matching performance. We present …

Auto: Scaling deep reinforcement learning for datacenter-scale automatic traffic optimization

L Chen, J Lingys, K Chen, F Liu - Proceedings of the 2018 conference of …, 2018 - dl.acm.org
Traffic optimizations (TO, eg flow scheduling, load balancing) in datacenters are difficult
online decision-making problems. Previously, they are done with heuristics relying on …

TIMELY: RTT-based congestion control for the datacenter

R Mittal, VT Lam, N Dukkipati, E Blem… - ACM SIGCOMM …, 2015 - dl.acm.org
Datacenter transports aim to deliver low latency messaging together with high throughput.
We show that simple packet delay, measured as round-trip times at hosts, is an effective …

CONGA: Distributed congestion-aware load balancing for datacenters

M Alizadeh, T Edsall, S Dharmapurikar… - Proceedings of the …, 2014 - dl.acm.org
We present the design, implementation, and evaluation of CONGA, a network-based
distributed congestion-aware load balancing mechanism for datacenters. CONGA exploits …

pFabric: Minimal near-optimal datacenter transport

M Alizadeh, S Yang, M Sharif, S Katti… - ACM SIGCOMM …, 2013 - dl.acm.org
In this paper we present pFabric, a minimalistic datacenter transport design that provides
near theoretically optimal flow completion times even at the 99th percentile for short flows …