Mininet, ONOS, and segment routing
This article will describe how to emulate a leaf and spine network using Mininet and configure the ONOS segment routing application to provide equal cost multi-path (ECMP) routing of flows across the fabric. The Mininet Dashboard application running on the sFlow-RT real-time analytics platform is used to provide visibility into traffic flows across the emulated network.
First, run ONOS using Docker:
docker run --name onos --rm -p 6653:6653 -p 8181:8181 -d onosproject/onosUse the graphical interface, http://onos:8181, to enable the OpenFlow Provider Suite, Network Config Host Provider, Network Config Link Provider, and Segment Routing applications. The screen shot above shows the resulting set of enabled services.
Next, install sFlow-RT and the Mininet Dashboard application on host with Mininet:
wget https://inmon.com/products/sFlow-RT/sflow-rt.tar.gzStart sFlow-RT:
tar -xvzf sflow-rt.tar.gz
./sflow-rt/get-app.sh sflow-rt mininet-dashboard
./sflow-rt/start.shDownload the sr.py script:
wget https://raw.githubusercontent.com/sflow-rt/onos-sr/master/sr.pyStart Mininet:
sudo env ONOS=10.0.0.73 mn --custom sr.py,sflow-rt/extras/sflow.py \
--link Continue reading
Real-time visibility at 400 Gigabits/s
The data was gathered from The International Conference for High PerformanceComputing, Networking, Storage, and Analysis (SC18) being held this week in Dallas. The conference network, SCinet, is described as the fastest and most powerful network in the world.
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| NRE-36 University of Southern California network topology for SuperComputing 2018 |
Providing the visibility needed to manage large scale high speed networks is a significant challenge. In this example, line rate traffic of 80 million packets per second is being monitored on the 400G port. The maximum packet rate for 64 byte packets on a 400 Gigabit, full duplex, link is approximately 1.2 billion packet per second Continue reading
Ryu measurement based control
This article recreates the demonstration using the Ryu SDN framework and emulating a network using Mininet. Install both pieces of software on a Linux server or virtual machine in order to follow this example.
Start Ryu with the simple_switch_13 and ryu.app.ofctl_rest applications loaded:
ryu-manager $RYU_APP/simple_switch_13.py,$RYU_APP/ofctl_rest.pyNote: The simple_switch_13.py and ofctl_rest.py scripts are part of a standard Ryu installation. The $RYU_APP variable has been set to point to the Ryu app directory.
Download sFlow-RT:
wget https://inmon.com/products/sFlow-RT/sflow-rt.tar.gzInstall the Mininet Dashboard application:
tar -xvzf sflow-rt.tar.gz
sflow-rt/get-app.sh sflow-rt mininet-dashboardThe following script, ryu.js, implements the DDoS mitigation function described in the previous article:
var ryu = '127.0.0.1';
var controls = {};
setFlow('udp_reflection',
{keys:'ipdestination,udpsourceport',value:'frames'});
setThreshold('udp_reflection_attack',
{metric:'udp_reflection',value:100,byFlow:true,timeout:2});
setEventHandler(function(evt) {
// don't consider inter-switch links
var link = topologyInterfaceToLink(evt.agent,evt.dataSource);
if(link) return;
// get port information
var port = topologyInterfaceToPort(evt.agent,evt.dataSource);
if(! Continue reading
Systemd traffic marking
This article describes a recent extension to the Host sFlow systemd module, mapping sampled traffic to the individual services the generate or consume them. The ability to color traffic by application greatly simplifies service discovery and service dependency mapping; making it easy to see how services communicate in a multi-tier application architecture.
The following /etc/hsflowd.conf file configures the Host sFlow agent, hsflowd, to sampling packets on interface eth0, monitor systemd services and mark the packet samples, and track tcp performance:
sflow {
collector { ip = 10.0.0.70 }
pcap { dev = eth0 }
systemd { markTraffic = on }
tcp { }
}The diagram above illustrates how the Host sFlow agent is able to efficiently monitor and classify traffic. In this case both the Host sFlow agent and an Apache web server are are running as services managed by systemd. A network connection , shown in Continue reading
Microsoft Office 365
This information is extremely useful, allowing traffic analytics software to combine telemetry obtained from network devices with information obtained using the Microsoft REST API in order to identifying clients, links, and devices carrying the traffic, as well as any issues, such as link errors, and congestion, that may be impacting performance.
var api = 'https://endpoints.office.com/endpoints/worldwide';
function uuidv4() {
return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {
var r = Math.random() * 16 | 0, v = c == 'x' ? r : (r & 0x3 | 0x8);
return v.toString(16);
});
}
var reqid = uuidv4();
function updateAddressMap() {
var res, i, ips, id, groups;
try { res = http(api+'?clientrequestid='+reqid); }
catch(e) { logWarning('request failed ' + e); }
if(res == null) return;
res = JSON.parse(res);
groups Continue reading
Northbound Networks Zodiac GX
Mininet is widely used to emulate software defined networks (SDNs). Mininet flow analytics describes how standard sFlow telemetry, from Open vSwitch used by Mininet emulate the network, provides feedback to an SDN controller, allowing the controller to adapt the network to changing traffic, for example, to mitigate a distributed denial of service (DDoS) attack.
Northbound Networks Zodiac GX is an inexpensive open source software based switch that is ideal for experimenting with software defined networking (SDN) in a physical network setting. The small fanless package makes the switch an attractive option for desktop use. The Zodiac GX is also based on Open vSwitch, making it easy to take SDN control strategies developed on Mininet.
Northbound Networks Zodiac GX is an inexpensive open source software based switch that is ideal for experimenting with software defined networking (SDN) in a physical network setting. The small fanless package makes the switch an attractive option for desktop use. The Zodiac GX is also based on Open vSwitch, making it easy to take SDN control strategies developed on Mininet.
ovs-vsctl -- --id=@sflow create sflow agent=$OVS_BR target=$IP_CONTROLLER_1 sampling=100 polling=10 -- set bridge $OVS_BR sflow=@sflowReboot the switch for the changed to take effect.
Use sflowtool to verify that sFlow is arriving at the controller host and to examine the contents of the telemetry stream. Running sflowtool using Docker is a simple alternative to building the software Continue reading
RDMA over Converged Ethernet (RoCE)
The sFlow telemetry stream includes packet headers, sampled at line rate by the switch hardware. Hardware packet sampling allows the switch to monitor traffic at line rate on all ports, keeping up with the high speed data transfers associated with RoCE.
The diagram above shows the packet headers associated with RoCEv1 and RoCEv2 packets. Decoding the InfiniBand Global Routing Header (IB GRH) and InfiniBand Base Transport Header (IB BTH) allows an sFlow analyzer to report in detail on RoCE traffic.
ExtremeXOS 22.5.1 adds support Broadcom ASIC table utilization statistics
Figure 1 shows the packet processing pipeline of a Broadcom ASIC. The pipeline consists of a number of linked hardware tables providing bridging, routing, access control list (ACL), and ECMP forwarding group functions. Operations teams need to be able to proactively monitor table utilizations in order to avoid performance problems associated with table exhaustion.
Broadcom's sFlow specification, sFlow Broadcom Switch ASIC Table Utilization Structures, leverages the industry standard sFlow protocol to offer scaleable, multi-vendor, network wide visibility into the utilization of these hardware tables.
The following output from the open source sflowtool command line utility shows the raw table measurements (this is in addition to the extensive set of measurements already exported via sFlow by ExtremeXOS):
bcm_asic_host_entries 4
bcm_host_entries_max 8192
bcm_ipv4_entries 0
bcm_ipv4_entries_max 0
bcm_ipv6_entries 0
bcm_ipv6_entries_max 0
bcm_ipv4_ipv6_entries 9
bcm_ipv4_ipv6_entries_max 16284
bcm_long_ipv6_entries 3
bcm_long_ipv6_entries_max 256
bcm_total_routes 10
bcm_total_routes_max 32768
bcm_ecmp_nexthops 0
bcm_ecmp_nexthops_max 2016
bcm_mac_entries 3
bcm_mac_entries_max 32768
bcm_ipv4_neighbors 4
bcm_ipv6_neighbors 0
bcm_ipv4_routes 0
bcm_ipv6_routes 0
bcm_acl_ingress_entries Continue reading
Visualizing real-time network traffic flows at scale
Particles provide an intuitive representation of network packets transiting the network from source to destination. The animation slows time so that the particle takes 10 seconds (instead of milliseconds) to transit the network. Groups of particles traveling the same path represent flows of packets between the hosts. Particle size and frequency are used to indicate the intensity of the traffic flowing on a path.
Particles don't follow straight lines, instead following quadratic Bézier curves around the center of the screen. Warping particle paths toward the center of the screen ensures that all paths are of similar length and visible - even if the start and end points are on the same axis.
The example above is from a site with over 500 network switches carrying hundreds of Gigabits of traffic. Internet, Customer, Site and Datacenter hosts have been assigned to the North, East, South and West sides respectively.
sFlow available on Juniper PTX series routers
sFlow functionality introduced on the PTX1000 and PTX10000 platforms—Starting in Junos OS Release 18.2R1, the PTX1000 and PTX10000 routers support sFlow, a network monitoring protocol for high-speed networks. With sFlow, you can continuously monitor tens of thousands of ports simultaneously. The mechanism used by sFlow is simple, not resource intensive, and accurate. - New and Changed Features
The recent article, sFlow available on Juniper MX series routers, describes how Juniper is extending sFlow support to include routers to provide visibility across their entire range of switching and routing products.
Universal support for industry standard sFlow as a base Junos feature reduces the operational complexity and cost of network visibility for enterprises and service providers. Real-time streaming telemetry from campus switches, routers, and data center switches, provides centralized, real-time, end-to-end visibility needed to troubleshoot, optimize, and account for network usage.
Analytics software is a critical factor in realizing the full benefits of sFlow monitoring. Choosing an sFlow analyzer discusses important factors to consider when selecting from the range of open source and commercial sFlow analysis tools.
SDKLT
Building the Demo App describes how to get started using a simulated Tomahawk device. Included, is a CLI that can be used to explore tables. For example, the following CLI output shows the attributes of the sFlow packet sampling table:
BCMLT.0> lt list -d MIRROR_PORT_ENCAP_SFLOW
MIRROR_PORT_ENCAP_SFLOW
Description: The MIRROR_PORT_ENCAP_SFLOW logical table is used to specify
per-port sFlow encapsulation sample configuration.
11 fields (1 key-type field):
SAMPLE_ING_FLEX_RATE
Description: Sample ingress flex sFlow packet if the generated sFlow random
number is greater than the threshold. A lower threshold leads to
higher sampling frequency.
SAMPLE_EGR_RATE
Description: Sample egress sFlow packet if the generated sFlow random number is
greater than the threshold. A lower threshold leads to
higher sampling frequency.
SAMPLE_ING_RATE
Description: Sample ingress sFlow packet if the generated sFlow random number is
greater than the threshold. A lower threshold leads to
higher sampling frequency.
SAMPLE_ING_FLEX_MIRROR_INSTANCE
Description: Enable to copy ingress flex sFlow packet samples to the ingress
mirror member using the sFlow mirror instance configuration.
SAMPLE_ING_FLEX_CPU
Description: Enable to copy ingress flex Continue reading
sFlow available on Juniper MX series routers
Understanding How to Use sFlow Technology for Network Monitoring on a MX Series Router lists the following benefits of sFlow Technology on a MX Series Router:
- sFlow can be used by software tools like a network analyzer to continuously monitor tens of thousands of switch or router ports simultaneously.
- Since sFlow uses network sampling (forwarding one packet from ‘n’ number of total packets) for analysis, it is not resource intensive (for example processing, memory and more). The sampling is done at the hardware application-specific integrated circuits (ASICs) and hence it is simple and more accurate.
- ACX5000 Universal Access Router
- EX Series Ethernet Switches
- MX Series 3D Universal Edge Routers
- NFX Series Network Services Platform
- OCX1100 Open networking, cloud-optimized switch
- QFX Series High-performance, high-density data center switches
Universal support for Continue reading
ONOS measurement based control
The following sFlow-RT script, ddos.js, detects DDoS attacks and programs ONOS filter rules to block the attacks:
var user = 'onos';
var password = 'rocks';
var onos = '192.168.123.1';
var controls = {};
setFlow('udp_reflection',
{keys:'ipdestination,udpsourceport',value:'frames'});
setThreshold('udp_reflection_attack',
{metric:'udp_reflection',value:100,byFlow:true,timeout:2});
setEventHandler(function(evt) {
// don't consider inter-switch links
var link = topologyInterfaceToLink(evt.agent,evt.dataSource);
if(link) return;
// get port information
var port = topologyInterfaceToPort(evt.agent,evt.dataSource);
if(!port) return;
// need OpenFlow info to create ONOS filtering rule
if(!port.dpid || !port.ofport) return;
// we already have Continue reading
ONOS traffic analytics
Mininet and onos.py workflow describes how to run ONOS using the Mininet network emulator. Mininet allows virtual networks to be quickly constructed and is a simple way to experiment with ONOS. In addition, Mininet flow analytics describes how to enable industry standard sFlow streaming telemetry in Mininet, proving a simple way monitor traffic in the ONOS controlled network.
For example, the following command creates a Mininet network, controlled by ONOS, and monitored using sFlow:
sudo mn --custom ~/onos/tools/dev/mininet/onos.py,sflow-rt/extras/sflow.py \The screen capture above shows the network topology in the ONOS web user interface.
--link tc,bw=10 --controller onos,1 --topo tree,2,2
In this case, the traffic was created by the following Mininet command:
mininet-onos> iperf h1 h3
Real-time baseline anomaly detection
Writing Applications provides a general introduction to sFlow-RT programming. The baseline functionality is exposed through through the JavaScript API.
Create new baseline
baselineCreate(name,window,sensitivity,repeat);Where:
- name, name used to reference baseline.
- window, the number of previous intervals to consider in calculating the limits.
- sensitivity, the number of standard deviations used to calculate the limits.
- repeat, the number of successive data points outside the limits before flagging anomaly
Update baseline
var status = baselineCheck(name,value);Where:
- status, "learning" while baseline is warming up (takes window intervals), "normal" if value is in expected range, "low" Continue reading
Flow smoothing
The traffic generator in this example creates an alternating pattern: 1.25Mbytes/second for 30 seconds followed by a pause of 30 seconds. Smoothing time constants between 1 second and 500 seconds have been applied to generate the family of charts. The blue line is the result of 1 second smoothing and closely tracks the traffic pattern. At the other extreme, the dark red line is the result of 500 second smoothing, showing a constant 625Kbytes/second (the average of the waveform).
There is a tradeoff between responsiveness and variability (noise) when selecting the level of smoothing. Selecting a suitable smoothing level depends on the flow analytics application.
Low smoothing values are appropriate when fast response is required, for example:
- DDoS detection
- Leaf and spine traffic engineering using segment routing and SDN
- Internet router using merchant silicon
- Real-time traffic visualization using Netflix Vizceral
- Real-time web analytics
- Real-time visibility and control of campus networks
Generating the chart
The results described in this article are easily reproduced using the testbed Continue reading
Mininet weathermap
The network was created using the following Mininet command:
sudo mn --custom=sflow-rt/extras/sflow.py --link tc,bw=10 \
--topo torus,3,3 --switch ovsbr,stp=1 --test iperf
sudo mn --custom sflow-rt/extras/sflow.py --link tc,bw=10 \
--topo tree,depth=4,fanout=2 --test iperf
sudo mn --custom sflow-rt/extras/sflow.py --link tc,bw=10 \It's also easy to create Custom Topologies. The following command creates the example custom topology, topo-2sw-2host.py, that ships with Mininet:
--topo linear,4 --test iperf
sudo mn --custom ~/mininet/custom/topo-2sw-2host.py,sflow-rt/extras/sflow.py Continue reading
OCP Summit 2018
The following articles on this blog cover related topics:
- Streaming telemetry
- InBand network telemetry
- Black hole detection
- Troubleshooting connectivity problems in leaf and spine fabrics
- Monitoring leaf and spine fabric performance
- Leaf and spine traffic engineering using segment routing and SDN
- Marking large flows
- Broadcom BroadView Instrumentation
- Broadcom ASIC table utilization metrics, DevOps, and SDN
- Lasers!
The rack in the booth contains a two spine, five leaf network. Each of the switches in the network Continue reading
Prometheus and Grafana
The diagram above shows the elements of the solution: sFlow telemetry streams from hosts and switches to an instance of sFlow-RT. The sFlow-RT analytics software converts the raw measurements into metrics that are accessible through a REST API.
The following prometheus.php script mediates between the Prometheus metrics export protocol and the sFlow-RT REST API. HTTP queries from Prometheus are translated into calls to the sFlow-RT REST API and JSON responses are converted into Prometheus metrics.
<?php
header('Content-Type: text/plain');
if(isset($_GET['labels'])) {
$keys = htmlspecialchars($_GET["labels"]);
}
$vals = htmlspecialchars($_GET["values"]);
if(isset($keys)) {
$cols = $keys.','.$vals;
} else {
$cols = $vals;
}
$key_arr = explode(",",$keys);
$result = file_get_contents('http://localhost:8008/table/ALL/'.$cols.'/json');
$obj = json_decode($result,true);
foreach ($obj as $row) {
unset($labels);
foreach ($row as $cell) {
if(!isset($labels)) {
$labels = 'agent="'.$cell['agent'].'",datasource="'.$cell['dataSource'].'"';
}
$name = $cell['metricName'];
$val = $cell['metricValue'];
if(in_array($name,$key_arr)) {
$labels .= Continue reading
OpenSwitch
This article describes how to enable industry standard sFlow telemetry using the open source Host sFlow agent. The Host sFlow agent uses Control Plane Services (CPS) to configure sFlow instrumentation in the hardware and gather metrics. CPS in turn uses the Open Compute Project (OCP) Switch Abstraction Interface (SAI) as a vendor independent method of configuring the hardware. Hardware support for sFlow is a standard feature supported by Network Processing Unit (NPU) vendors (Barefoot, Broadcom, Cavium, Innovium, Intel, Marvell, Mellanox, etc.) and vendor neutral sFlow configuration is part of the SAI.
Installing and configuring Host sFlow agent
Installing the software is simple. Log into the switch and type the following commands:wget --no-check-certificate https://github.com/sflow/host-sflow/releases/download/v2.0.17-1/hsflowd-opx_2.0.17-1_amd64.debThe sFlow agent requires very little configuration, automatically monitoring all switch ports using the following default settings:
sudo dpkg -i hsflowd-opx_2.0.17-1_amd64.deb
| Link Speed | Sampling Rate | Polling Continue reading |
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