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 |
|---|
Intranet DDoS attacks
As on a Darkling Plain: Network Survival in an Age of Pervasive DDoS talk by Steinthor Bjarnason at the recent NANOG 71 conference. The talk discusses the threat that the proliferation of network connected devices in enterprises create when they are used to launch denial of service attacks. Last year's Mirai attacks are described, demonstrating the threat posed by mixed mode attacks where a compromised host is used to infect large numbers devices on the corporate network.
In both cases, most of the critical activity occurs behind the corporate firewall, making it extremely challenging to detect and mitigate these threats.
The talk discusses a number of techniques that service providers use to secure their networks that enterprises will need to adopt Continue reading
RESTful control of Cumulus Linux ACLs
The Github pphaal/acl_server project INSTALL page describes how to install the acl_server daemon and configure the NGINX web server front end for the Cumulus Linux REST API to include the acl_server functions. The integration ensures that the same access controls configured for the REST API apply to the acl_server functions, which appear under the /acl/ path.
The following examples demonstrate the REST API.
Create an ACL
curl -X PUT -H 'Content-Type:application/json' --data '["[iptables]","-A FORWARD --in-interface swp+ -d 10.10.100.10 -p udp --sport 53 -j DROP"]' -k -u 'cumulus:CumulusLinux!' https://10.0.0.52:8080/acl/ddos1ACLs are sent as a JSON encoded array of strings. Each string will be written as a line in a file stored under /etc/cumulus/acl/policy.d/ - See Cumulus Linux: Netfilter - ACLs. For example, the rule above will be written to the file 50rest-ddos1.rules with the following Continue reading
Real-time WiFi heat map
Real-time Wifi-Traffic Heatmap (source code GitHub: cod3monk/showfloor-heatmap) displays real-time WiFi traffic from SC17 (The International Conference for High Performance Computing, Networking, Storage and Analysis, November 12-17, 2017). Click on the link to see live data.The Cisco Wireless access points in the conference network don't currently support sFlow, however, the access points are connected to Juniper EX switches which stream sFlow telemetry to an instance of sFlow-RT analytics software that provides real-time usage metrics for the heat map.
Wireless describes the additional visibility delivered by sFlow capable wireless access points, including: air time, channel, retransmissions, receive / transmit speeds, power, signal to noise ratio, etc. With sFlow enabled wireless access points, additional information could be layered on the heat map. The sFlow.org web site lists network products and vendors that support the sFlow standard.
Arista EOS CloudVision
Fabric Visibility on Arista EOS Central describes how to use industry standard sFlow instrumentation in Arista switches to deliver real-time flow analytics. This article describes the steps needed to integrate flow analytics into CloudVision.
Log into the CloudVision node and run the following cvp_install_fabricview.sh script as root:
#!/bin/shEdit the cvp-api-topology.py script to Continue reading
# Install Fabric View on CloudVision Portal (CVP)
VER=`wget -qO - http://inmon.com/products/sFlow-RT/latest.txt`
wget http://www.inmon.com/products/sFlow-RT/sflow-rt-$VER.noarch.rpm
rpm --nodeps -ivh sflow-rt-$VER.noarch.rpm
/usr/local/sflow-rt/get-app.sh sflow-rt fabric-view
ln -s /cvpi/jdk/bin/java /usr/bin/java
sed -i '/^# http.hostname=/s/^# //' /usr/local/sflow-rt/conf.d/sflow-rt.conf
echo "http.html.redirect=./app/fabric-view/html/" >> /usr/local/sflow-rt/conf.d/sflow-rt.conf
cat <<EOT > /etc/nginx/conf.d/locations/sflow-rt.https.conf
location /sflow-rt/ {
auth_request /aeris/auth;
proxy_buffering off;
proxy_set_header X-Forwarded-For \$proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Prefix /sflow-rt/;
proxy_set_header Host \$host;
proxy_pass http://localhost:8008/;
proxy_redirect ~^http://[^/]+(/.+)\$ /sflow-rt\$1;
}
EOT
systemctl restart nginx.service
firewall-cmd --zone public --add-port=6343/udp --permanent
firewall-cmd --reload
systemctl enable sflow-rt.service
systemctl start sflow-rt.service
wget http://www.inmon.com/products/sFlow-RT/cvp-eapi-topology.py
chmod +x cvp-eapi-topology.py
echo "configure and run cvp-eapi-topology.py"
Real-time visibility and control of campus networks
HPE Aruba has an extensive selection of campus switches that combine programmatic control via a REST API with hardware sFlow support:
- Aruba 2530
- Aruba 2540
- Aruba 2620
- Aruba 2930F
- Aruba 2930M
- Aruba 3810
- Aruba 5400R
- Aruba 8400
Peer-to-peer protocols (P2P) pose some unique challenges:
- P2P protocols make use of very large numbers of connections in order to quickly transfer data. The large number of connections allows a P2P user to obtain a disproportionate amount of network bandwidth; even a small number of P2P users (less than 0.5% of users) can consume over 90% of the network bandwidth.
- P2P protocols (and users) are very good Continue reading
Flow Trend
docker run -p 6343:6343/udp -p 8008:8008 sflow/flow-trendThe simplest way to run the software is using the docker. Configure network devices to send standard sFlow telemetry to Flow Trend. Access the web user interface on port 8008.
Real-time traffic visualization using Netflix Vizceral
The simplest way to run the software is to use the pre-built Docker image:
docker run -p 6343:6343/udp -p 8008:8008 sflow/vizceralThe Docker image also contains demo data based on Netflix's public cloud infrastructure:
docker run -e "RTPROP=-Dviz.demo=yes" -p 8008:8008 sflow/vizceral
Troubleshooting connectivity problems in leaf and spine fabrics
Unlike traditional hierarchical network designs, where a small number of links can be monitored to provide visibility, a leaf and spine network has no special links or switches where running CLI commands or attaching a probe would provide visibility. Even if it were possible to attach probes, the effective bandwidth of a leaf and spine network can be as high as a Petabit/second, well beyond the capabilities of current generation monitoring tools.
Fortunately, industry standard sFlow monitoring technology is built into the commodity switch hardware used to build leaf and spine networks. Enabling sFlow telemetry on all the switches in the network provides centralized, real-time, visibility into network traffic.
Cumulus Linux 3.4 REST API
The following ddos.js script is modified to use the REST API to send Network Command Line Utility - NCLU commands to add and remove ACLs, see Installing and Managing ACL Rules with NCLU:
var user = "cumulus";
var password = "CumulusLinux!";
var thresh = 10000;
var block_minutes = 1;
setFlow('udp_target',{keys:'ipdestination,udpsourceport',value:'frames'});
setThreshold('attack',{metric:'udp_target', value:thresh, byFlow:true, timeout:10});
function restCmds(agent,cmds) {
for(var i = 0; i < cmds.length; i++) {
let msg = {cmd:cmds[i]};
http("https://"+agent+":8080/nclu/v1/rpc",
"post","application/json",JSON.stringify(msg),user,password);
}
}
var controls = {};
var id = 0;
setEventHandler(function(evt) {
var key = evt.agent + ',' + evt.flowKey;
if(controls[key]) return;
var ifname = metric(evt.agent,evt.dataSource+".ifname")[0].metricValue;
if(!ifname) return;
var now = (new Date()).getTime();
var name = 'ddos'+id++;
var [ip,port] = evt.flowKey.split(',');
var cmds = [
'add acl ipv4 '+name+' drop udp source-ip any source-port '+port+' dest-ip '+ip+' dest-port any',
Continue reading