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Real-time domain name lookups
First, use the dns.servers System Property is used to specify one or more DNS servers to handle the reverse lookup requests. For example, the following command uses Docker to run sFlow-RT with DNS lookups directed to server 10.0.0.1:
docker run -e "RTPROP=-Ddns.servers=10.0.0.1" \The following Python script dnspair.py uses the sFlow-RT REST API to define a flow and log the resulting flow records:
-p 8008:8008 -p 6343:6343/udp -d sflow/sflow-rt
#!/usr/bin/env pythonRunning the script generates the following output:
import requests
import json
flow = {'keys':'dns:ipsource,dns:ipdestination',
'value':'bytes','activeTimeout':10,'log':True}
requests.put('http://localhost:8008/flow/dnspair/json',data=json.dumps(flow))
flowurl = 'http://localhost:8008/flows/json?name=dnspair&maxFlows=10&timeout=60'
flowID = -1
while 1 == 1:
r = requests.get(flowurl + "&flowID=" + str(flowID))
if r.status_code != 200: break
flows = r.json()
if len(flows) == 0: continue
flowID = flows[0]["flowID"]
flows.reverse()
for f in flows:
print json.dumps(f,indent=1)
$ ./dnspair.py
{
"value": 233370.92322668363,
"end": 1476234478177,
"name": "dnspair",
"flowID": Continue reading
Collecting Docker Swarm service metrics
In this example, Docker Swarm is used to deploy a simple web service on a four node cluster:
docker service create --replicas 2 -p 80:80 --name apache httpd:2.4Next, the following script tests the agility of monitoring systems by constantly changing the number of replicas in the service:
#!/bin/bashThe above test is easy to set up and is a quick way to stress test monitoring systems and reveal accuracy and performance problems when they are confronted with container workloads.
while true
do
docker service scale apache=$(( ( RANDOM % 20 ) + 1 ))
sleep 30
done
Many approaches to gathering and recording metrics were developed for static environments and have a great deal of difficulty tracking rapidly changing container-based service pools without missing information, leaking resources, and slowing down. For example, each new container in Docker Swarm has unique name, e.g. apache.16.17w67u9157wlri7trd854x6q0. Monitoring solutions that record container names, or even worse, index data by container name, will suffer from bloated Continue reading
Docker 1.12 swarm mode elastic load balancing
Docker Built-In Orchestration Ready For Production: Docker 1.12 Goes GA describes the native swarm mode feature that integrates cluster management, virtual networking, and policy based deployment of services.
This article will demonstrate how real-time streaming telemetry can be used to construct an elastic load balancing solution that dynamically adjusts service capacity to match changing demand.
Getting started with swarm mode describes the steps to configure a swarm cluster. For example, following command issued on any of the Manager nodes deploys a web service on the cluster:
docker service create --replicas 2 -p 80:80 --name apache httpd:2.4And the following command raises the number of containers in the service pool from 2 to 4:
docker service scale apache=4Asynchronous Docker metrics describes how sFlow telemetry provides the real-time visibility required for elastic load balancing. The diagram shows how streaming telemetry allows the sFlow-RT controller to determine the load on the service pool so that it can use the Docker service API to automatically increase or decrease the size of the pool as demand changes. Elastic load balancing of the service pools ensures consistent service levels by adding additional resources if demand increases. In addition, efficiency is improved by releasing resources Continue reading
Asynchronous Docker metrics
This article describes how the latest version of the Host sFlow agent is able to track the performance of a rapidly changing population of Docker containers and export a real-time stream of standard sFlow metrics.
The diagram at the beginning of this article shows the sequence of messages, going from top to bottom, required to track a container. The Host sFlow agent first registers for container Continue reading
Triggered remote packet capture using filtered ERSPAN
Traditional hierarchical network designs were relatively straightforward to monitor using a packet broker since traffic flowed through a small number of core switches and so a small number of taps provided network wide visibility. The move to leaf and spine fabric architectures eliminates the performance bottleneck of core switches to deliver low latency and high bandwidth connectivity to data center applications. However, traditional packet brokers are less attractive since spreading traffic across many links with equal cost multi-path (ECMP) routing means that many more links need to be monitored.
This article will explore how the remote Selective Spanning capability in Cumulus Linux 3.0 combined with industry standard sFlow telemetry embedded in commodity switch hardware provides a cost effective alternative to traditional packet brokers.
Cumulus Linux uses iptables rules to specify packet capture sessions. For example, the following rule forwards packets with source IP 20.0.1.0 and destination IP 20.0.1.2 to a packet analyzer on host 20.0.2.2:
-A FORWARD --in-interface swp+ -s 20.0.0.2 -d 20. Continue reading
Real-time web analytics
The following sFlow-RT metrics report request rates based on the standard sFlow HTTP counters:
- http_method_option
- http_method_get
- http_method_head
- http_method_post
- http_method_put
- http_method_delete
- http_method_trace
- http_method_connect
- http_method_other
- http_status_1xx
- http_status_2xx
- http_status_3xx
- http_status_4xx
- http_status_5xx
- http_status_other
- http_requests
- workers_active
- workers_idle
- workers_max
- workers_utilization
- req_delayed
- req_dropped
http://localhost:8008/metric/ALL/sum:http_method_get,sum:http_method_post/jsonMore interesting is that the sFlow telemetry stream also includes randomly sampled HTTP request records with the following attributes:
- protocol
- serveraddress
- serveraddress6
- serverport
- clientaddress
- clientaddress6
- clientport
- proxyprotocol
- proxyserveraddress
- proxyserveraddress6
- proxyserverport
- proxyclientaddress
- proxyclientaddress6
- proxyclientport
- httpmethod
- httpprotocol
- httphost
- httpuseragent
- httpxff
- httpauthuser
- httpmimetype
- httpurl
- httpreferer
- httpstatus
- Continue reading
Network and system analytics as a Docker service
The sFlow-RT analytics software is now on Docker Hub, making it easy to deploy real-time sFlow analytics as a Docker service:
docker run -p 8008:8008 -p 6343:6343/udp -d sflow/sflow-rtConfigure standard sFlow Agents to stream telemetry to the analyzer and retrieve analytics using the REST API on port 8008.
Increase memory from default 1G to 2G:
docker run -e "RTMEM=2G" -p 8008:8008 -p 6343:6343/udp -d sflow/sflow-rtSet System Property to enable country lookups when Defining Flows:
docker run -e "RTPROP=-Dgeo.country=resources/config/GeoIP.dat" -p 8008:8008 -p 6343:6343/udp -d sflow/sflow-rtRun sFlow-RT Application. Drop the -d option while developing an application to see output of logging commands and use control-c to stop the container.
docker run -v /Users/pp/my-app:/sflow-rt/app/my-app -p 8008:8008 -p 6343:6343/udp -d sflow/sflow-rtA simple Dockerfile can be used to generate a new image that includes the application:
FROM sflow/sflow-rt:latestSimilarly, Continue reading
COPY /Users/pp/my-app /sflow-rt/app
Internet router using Cumulus Linux
First, add the Debian Jessie repository:
sudo sh -c 'echo "deb http://ftp.us.debian.org/debian jessie main contrib" > \Next, install Host sFlow, Java, and Bird:
/etc/apt/sources.list.d/deb.list'
sudo apt-get updateInstall sFlow-RT (the latest version is available at sFlow-RT.com):
sudo apt-get install hsflowd
sudo apt-get install unzip
sudo apt-get install default-jre-headless
sudo apt-get install bird
wget http://www.inmon.com/products/sFlow-RT/sflow-rt_2.0-1116.debIncrease the default virtual memory limit for sflowrt (needs to be greater than 1/3 amount of RAM on system to start Java virtual machine, see Giant Bug: Cannot run java with a virtual mem limit (ulimit -v)):
sudo dpkg -i sflow-rt_2.0-1116.deb
sudo sh -c 'echo "sflowrt soft as 2000000" > \Note: Maximum Java heap memory has a default of 1G and is controlled by settings in /usr/local/sflow-rt/conf.d/sflow-rt.jvm file.
/etc/security/limits.d/99-sflowrt.conf'
Install the Active Route Manager application:
sudo sh -c "/usr/local/sflow-rt/get-app. Continue reading
World map
Download and install sFlow-RT to run the world-map application. Enable the System Property, geo.country=resources/config/GeoIP.dat, to allow the application to identify countries based on IP addresses.
Internet router using merchant silicon
This article describes a simple self contained solution that uses standard APIs and should be able to run on a variety of Linux based network operating systems, including: Cumulus Linux, Dell OS10, Arista EOS, and Cisco NX-OS. The distinguishing feature of this solution is its real-time response, where previous solutions respond to changes in traffic within minutes or hours, this solution updates hardware routes within seconds.
The diagram shows the elements of the solution. Standard sFlow instrumentation embedded in the merchant silicon ASIC data plane in the white box switch provides real-time information on traffic flowing through the switch. The sFlow agent is configured to send the sFlow to an instance Continue reading
Network, host, and application monitoring for Amazon EC2
However, there are limitations on the types of traffic that are logged, a 10-15 minute delay in accessing flow records, and costs associated with using VPC and storing the logs in CloudWatch (currently $0.50 per GB ingested, $0.03 per GB archived per month, and possible addition Data Transfer OUT charges).
In addition, collecting basic host metrics at 1 minute granularity using CloudWatch is an additional $3.50 per instance per month.
The open source Host sFlow agent offers an alternative:
- Lightweight, requiring minimal CPU and memory on EC2 instances.
- Real-time, up to the second network visibility
- Efficient, export of extensive set of host metrics every 10-60 seconds (configurable).
$ cat /etc/issueThe following commands build the latest Continue reading
Amazon Linux AMI release 2016.03
Real-time BGP route analytics
- sFlow real-time traffic data identifies active BGP routes
- BGP path attributes are available in flow definitions
Setup
First download sFlow-RT. Next create a configuration file, bgp.js, in the sFlow-RT home directory with the following contents:var reflectorIP = '10.0.0.254';The following sFlow-RT System Properties load the configuration file and enable BGP:
var myAS = '65162';
var myID = '10.0.0.162';
var sFlowAgentIP = '10.0.0.253';
// allow BGP connection from reflectorIP
bgpAddNeighbor(reflectorIP,myAS,myID);
// direct sFlow from sFlowAgentIP to reflectorIP routing table
// calculate a 60 second moving average byte rate for each route
bgpAddSource(sFlowAgentIP,reflectorIP,60,'bytes');
- script.file=bgp.js
- bgp.start=yes
Continue reading
Configuring OpenSwitch
The following configuration enables sFlow monitoring of all interfaces on a white box switch running the OpenSwitch operating system, sampling packets at 1-in-4096, polling counters every 20 seconds and sending the sFlow to an analyzer (10.0.0.50) on UDP port 6343 (the default sFlow port):switch(config)# sflow collector 10.0.0.50A previous posting discussed the selection of sampling rates. Additional information can be found in the OpenSwitch sFlow User Guide.
switch(config)# sflow sampling 4096
switch(config)# sflow polling 20
switch(config)# sflow enable
See Trying out sFlow for suggestions on getting started with sFlow monitoring and reporting.
Cisco Tetration analytics
Cisco Tetration Analytics Data Sheet describes the hardware requirements:
And the power requirements:
No pricing is given, but based on the hardware, data center space, power and cooling requirements, this brute force approach to analytics will be reassuringly expensive to purchase and operate.
Update June 22, 2016: See 451 Research report, Cisco Tetration: a $3m, 1,700-pound appliance for network traffic analytics is born, for pricing information.
Programmable hardware: Barefoot Networks, PISA, and P4
table sflow_ing_take_sample {
/* take_sample > MAX_VAL_31 and valid sflow_session_id => take the sample */
reads {
ingress_metadata.sflow_take_sample : ternary;
sflow_metadata.sflow_session_id : exact;
}
actions {
nop;
sflow_ing_pkt_to_cpu;
}
}Network visibility is one of the major use cases for P4 based switches. Improving Network Monitoring and Management with Programmable Data Planes describes how P4 can be used to collect information about latency and queueing in the switch forwarding pipeline.
Merchant silicon based routing, flow analytics, and telemetry
The router market is poised to be similarly disrupted with the introduction of devices based on Broadcom's Jericho ASIC, which has the capacity to handle over 1 million routes in hardware (the full Internet routing table is currently around 600,000 routes).
An edge router is a very pricey box indeed, often costing anywhere from $100,000 to $200,000 per 100 Gb/sec port, depending on features in the router and not including optical cables that are also terribly expensive. Moreover, these routers might only be able to cram 80 ports into a half rack or full rack of space. The 7500R universal spine and 7280R universal leaf switches cost on the order of $3,000 per 100 Gb/sec port, and they are considerably denser and less expensive. - Leaving Fixed Function Switches Behind For Universal LeafsBroadcom Jericho ASICs are currently available in Arista 7500R/7280R routers and in Cisco NCS 5000 series routers. Expect further disruption Continue reading
Docker networking with IPVLAN and Cumulus Linux
Eliminating the complexity associated with switching broadcast domains, VLANs, spanning tree, etc. allows a purely routed network to be easily scaled to very large sizes. However, there are some challenges to overcome:
IPVlan will require routes to be distributed to each endpoint. The driver only builds the Ipvlan L3 mode port and attaches the container to the interface. Route distribution throughout a cluster is beyond the initial implementation of this single host scoped driver. In L3 mode, the Docker host is very similar to a router starting new networks in the container. They are on networks that the upstream network will not know about without route distribution.Cumulus Networks has been working to simplify routing in the ECMP leaf and spine networks and the white paper Routing on the Host: An Introduction shows how the routing configuration used on Cumulus Linux can be extended to the hosts.
Update June 2, 2016: Routing on the Host contains packaged versions of the Continue reading
Streaming telemetry
The OpenConfig project extends beyond configuration, "Streaming telemetry is a new paradigm for network monitoring in which data is streamed from devices continuously with efficient, incremental updates. Operators can subscribe to the specific data items they need, using OpenConfig data models as the common interface."
Anees Shaikh's Network Field Day talk provides an overview of OpenConfig and includes an example that demonstrates how configuration and state are combined in a single YANG data model. In the example, read/write config attributes used to configure a network interface (name, description, MTU, operational state) are combined with the state attributes needed to verify the configuration (MTU, name, description, oper-status, last-change) and collect metrics (in-octets, in-ucast-pkts, in-broadcast-pkts, ...).
Anees positions OpenConfig streaming telemetry mechanism as an attractive alternative to polling for metrics using Simple Network Management Protocol (SNMP) - see Push vs Pull for a detailed comparison between pushing (streaming) and pulling (polling) metrics.
Streaming telemetry is Continue reading
Internet of Things (IoT) telemetry
The recently released Raspberry Pi Zero (costing $5) is an example of the type of embedded low power computer enabling IoT. These small devices are typically wired to one or more sensors (measuring temperature, humidity, location, acceleration, etc.) and embedded in or attached to physical devices.
Collecting real-time telemetry from large numbers of small devices that may be located within many widely dispersed administrative domains poses a number of challenges, for example:
- Discovery - How are newly connected devices discovered?
- Configuration - How can the numerous individual devices be efficiently configured?
- Transport - How efficiently are measurements transported and delivered?
- Latency - How long does it take before measurements are remotely accessible?
The following steps describe how to install the Host sFlow Continue reading
OVS Orbit podcast with Ben Pfaff
Follow the link to see listen to the podcast, read the extensive show notes, follow related links, and to subscribe to the podcast.