Leveraging deep reinforcement learning for traffic engineering: A survey
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
SketchINT: Empowering INT with TowerSketch for per-flow per-switch measurement
Network measurement is indispensable to network operations. INT solutions that can
provide fine-grained per-switch per-packet information serve as promising solutions for per …
provide fine-grained per-switch per-packet information serve as promising solutions for per …
One more config is enough: Saving (DC) TCP for high-speed extremely shallow-buffered datacenters
The link speed in production datacenters is growing fast, from 1 Gbps to 40 Gbps or even
100 Gbps. However, the buffer size of commodity switches increases slowly, eg, from 4 MB …
100 Gbps. However, the buffer size of commodity switches increases slowly, eg, from 4 MB …
Flow scheduling with imprecise knowledge
Most existing data center network (DCN) flow scheduling solutions aim to minimize flow
completion times (FCT). However, these solutions either require precise flow information …
completion times (FCT). However, these solutions either require precise flow information …
Enabling load balancing for lossless datacenters
Various datacenter network (DCN) load balancing schemes have been proposed in the past
decade. Unfortunately, most of these solutions designed for lossy DCNs do not work well for …
decade. Unfortunately, most of these solutions designed for lossy DCNs do not work well for …
A receiver-driven transport protocol with high link utilization using anti-ECN marking in data center networks
Existing reactive or proactive congestion control protocols are hard to simultaneously
achieve ultra-low latency and high link utilization across all workloads ranging from delay …
achieve ultra-low latency and high link utilization across all workloads ranging from delay …
DRL-PLink: Deep reinforcement learning with private link approach for mix-flow scheduling in software-defined data-center networks
WX Liu, J Lu, J Cai, Y Zhu, S Ling… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In datacenter networks, bandwidth-demanding elephant flows without deadline and delay-
sensitive mice flows with strict deadline coexist. They compete with each other for limited …
sensitive mice flows with strict deadline coexist. They compete with each other for limited …
Efficient data-plane memory scheduling for in-network aggregation
As the scale of distributed training grows, communication becomes a bottleneck. To
accelerate the communication, recent works introduce In-Network Aggregation (INA), which …
accelerate the communication, recent works introduce In-Network Aggregation (INA), which …
Flash: Joint Flow Scheduling and Congestion Control in Data Center Networks
Flow scheduling and congestion control are two important techniques to reduce flow
completion time in data center networks. While existing works largely treat them …
completion time in data center networks. While existing works largely treat them …
Towards fine-grained load balancing with dynamical flowlet timeout in datacenter networks
In modern datacenter networks (DCNs), load balancing mechanisms are widely deployed to
enhance link utilization and alleviate congestion. Recently, a large number of load …
enhance link utilization and alleviate congestion. Recently, a large number of load …