LibreQoS: A Rust repository from magespawn

LibreQoS is a Quality of Experience (QoE) Smart Queue Management (SQM) system designed for Internet Service Providers to optimize the flow of their network traffic and thus reduce bufferbloat, keep the network responsive, and improve the end-user experience.

Servers running LibreQoS can shape traffic for many thousands of customers.

Learn more at LibreQoS.io!

Support LibreQoS

Please support the continued development of LibreQoS by sponsoring us via GitHub Sponsors or Patreon.

Matrix Chat

Our Matrix chat channel is available at https://matrix.to/#/#libreqos:matrix.org.

Features

Flexible Hierarchical Shaping / Back-Haul Congestion Mitigation

Starting in version v1.1+, operators can map their network hierarchy in LibreQoS. This enables both simple network hierarchies (Site>AP>Client) as well as much more complex ones (Site>Site>Micro-PoP>AP>Site>AP>Client). This can be used to ensure that a given site’s peak bandwidth will not exceed the capacity of its back-haul links (back-haul congestion control). Operators can support more users on the same network equipment with LibreQoS than with competing QoE solutions which only shape by AP and Client.

CAKE

CAKE is the product of nearly a decade of development efforts to improve on fq_codel. With the diffserv_4 parameter enabled – CAKE groups traffic in to Bulk, Best Effort, Video, and Voice. This means that without having to fine-tune traffic priorities as you would with DPI products – CAKE automatically ensures your clients’ OS update downloads will not disrupt their zoom calls. It allows for multiple video conferences to operate on the same connection which might otherwise “fight” for upload bandwidth causing call disruptions. With work-from-home, remote learning, and tele-medicine becoming increasingly common – minimizing video call disruptions can save jobs, keep students engaged, and help ensure equitable access to medical care.

XDP

Fast, multi-CPU queueing leveraging xdp-cpumap-tc and cpumap-pping. Currently tested in the real world past 11 Gbps (so far) with just 30% CPU use on a 16 core Intel Xeon Gold 6254. It's likely capable of 30Gbps or more.

Graphing

You can graph bandwidth and TCP RTT by client and node (Site, AP, etc), using InfluxDB.

CRM Integrations

UISP
Splynx

Server Recommendations

It is most cost-effective to buy a used server with specifications matching your unique requirements, as laid out in the System Requirements section below. For those who do not have the time to do that, here is a server config for up to 20 Gbps throughput:

20 Gbps | Dell R450 Config

The AsRock 1U4LW-B6502L2T can be a great lower-cost option as well.

System Requirements

VM or physical server

For VMs, NIC passthrough is required for optimal throughput and latency (XDP vs generic XDP). Using Virtio / bridging is much slower than NIC passthrough. Virtio / bridging should not be used for large amounts of traffic.

CPU

2 or more CPU cores
A CPU with solid single-thread performance within your budget. Queuing is very CPU-intensive, and requires high single-thread performance.

Single-thread CPU performance will determine the max throughput of a single HTB (cpu core), and in turn, what max speed plan you can offer customers.

Customer Max Plan	Passmark Single-Thread
100 Mbps	1000
250 Mbps	1500
500 Mbps	2000
1 Gbps	2500
2 Gbps	3000

Below is a table of approximate aggregate throughput capacity, assuming a a CPU with a single thread performance of 2700 or greater:

Aggregate Throughput	CPU Cores
500 Mbps	2
1 Gbps	4
5 Gbps	6
10 Gbps	8
20 Gbps	16
50 Gbps*	32

(* Estimated)

So for example, an ISP delivering 1Gbps service plans with 10Gbps aggregate throughput would choose a CPU with a 2500+ single-thread score and 8 cores, such as the Intel Xeon E-2388G @ 3.20GHz.

Memory

Minimum RAM = 2 + (0.002 x Subscriber Count) GB
Recommended RAM:

Subscribers	RAM
100	4 GB
1,000	8 GB
5,000	16 GB
10,000*	18 GB
50,000*	24 GB