How to monitor F5 devices – SNMP vs API vs SSH

F5 has many ways of interfacing with their products and when writing monitoring we had to do some research which one is more suitable in terms of performance. After all, monitoring should not harm the device it monitors. When choosing methods we looked into iControl REST, SNMP and TMSH. See below for how this test was conducted and which one won.

The best way to monitor F5 – How the test was conducted

We ran each type ~20 minutes continuously through command-runner. While running the tests the web interface was used to make sure that the web interface responsiveness was up to par.

The commands to run each test

#REST
while true; do
command-runner.sh full-command –basic-authentication user,password rest-pool-statistics.ind 10.10.10.10
done
#tmsh
while true; do
command-runner.sh full-command –ssh user,password ./show-ltm-pool-detail-raw-recursive.ind 10.10.10.10
done
#SNMP
while true; do
command-runner.sh full-command –ssh user,password ./snmp-pool-statistics.ind 10.10.10.10
done

Results

The test started out with 283 pools (with 200 additional ones created just for this test). However, when trying the tmsh command, command-runner timed out, so we had to reduce to the original 83 pools and rerun the test using rest to make it fair.

  • Test 1: REST = 283 pools
  • Test 2: Tmsh = 83 pools
  • Test 3: SNMP = 83 pools
  • Test 4: REST (take 2) = 83 pools

4 hour graph

24 hour graph for reference

REST

  • Did not produce any timeouts in the GUI in any of the two tests.
  • Always produced results.
  • Management interface only became sluggish one time during the second attempt. Most likely because of the already high swap usage created by the TMSH tests.

TMSH

TMSH produced these once in awhile:

  • When that happened you can see the gaps in the graph. It is unknown what the gap after the graph was because we was working on the snmp metrics at that time.
  • TMSH also failed to give results sometimes.
  • Forced to run with fewer metrics than rest in order to even get a result.

SNMP

  • Truncated the pool names sometimes. It is unclear why ast was always done on long names, but different lengths.
  • Did not produce any timeouts in the GUI.
  • Always produced results.
  • Did not have as many metrics as REST since the exact same metrics was not available in one command (pool state and availability is missing).
  • Management interface became a bit sluggish on and off.

Conclusion

Over all REST won the test with SNMP as second. TMSH did not even qualify as it takes up very large amounts of memory and swap which negatively affected the overall system.

Thank you to Patrik Jonsson for contributing this article.

How to select script monitoring authentication types

Considerations when selecting authentication types

Choosing an authentication method for monitoring your infrastructure devices might sound easy at first glance. After all, a monitoring script would only need read-only, right? Wrong.

Monitoring with indeni goes beyond what normal monitoring tools does. The goal of indeni is to detect problems before they occur, saving you hours of troubleshooting and root cause analysis down the road. To get early detection indeni needs access to the advanced shell. Let’s take a look at what this means on F5 devices.

Example: Selecting authentication types for F5 devices

On an F5, having access to the advanced shell means that the user in question must have administrator access. Also, iControl REST requires the user to be locally authenticated up until version 11.5.4. This means that for systems running versions up to 11.5.4 using RADIUS for authentication administrators will have to resort to the local admin account for REST calls.

On top of that if a system has configured authentication and authorization using RADIUS there is no way of setting the shell to advanced shell on any version. So yet again, administrators must resort to the local admin account in order to set the proper permissions.
We have gone above and beyond to avoid using local admin accounts by investing a lot of time running monitor commands via TMSH. However, this has turned out to cause harm to the system due to TMSH using way too much memory. So what does this mean? In order for get the most out of using indeni, administrators will have to configure authentication according to the following table:

Version
Authentication
Authorization
User
11.5.4 and earlier
Any
Any
Local admin (with SSH access)
11.6.0 and later
Remote
Remote
Local admin (with SSH access)
11.6.0 and later
Local
Local
Any account with role Administrator and shell set to Advanced Shell
11.6.0 and later
Remote
Local
Any account with role Administrator and shell set to Advanced Shell

Thank you to Patrik Jonsson for contributing this article.

Machine learning for logs, cut through the hype.

 

Splunk recently announced new machine learning capabilities in its Splunk Cloud and Splunk Enterprise 6.5 release. Does everyone have machine learning capabilities now? What exactly is machine learning? See below for key considerations for this technology approach and how indeni’s machine learning differs from Splunk.

3 things IT needs to know about machine learning


  • Machine learning algorithms have been around for decades. Most of them, especially those that are mathematically based, are not new. For example Arthur Samuel coined the term “machine learning” in 1959!
  • Machine learning works best with large sets of data. You need a substantial amount of information to determine trends, correlations, etc. Take the example of the NVIDIA self-driving car that was shown at CES this year. Only after 3000 miles of driving on highways, back roads and suburban roads was the car able to stop running over traffic cones and avoid parked cars.
  • If not constrained, Machine learning will have a very high false positive rate. To continue the analogy from above, say you are monitoring multiple types of automobiles. Comparing the device data of a semi-truck to a Tesla would be interesting, but not actionable. Say one of your rules was to alert if the engine noise exceeded 100 decibels, as you believe this level of noise indicates there is an issue with the engine. A semi-truck would generate an alert every time it turned on, whereas a Tesla would hardly say a peep. Giving your machine learning constraints (eg. compare Tesla data only with other Tesla’s) yields far more accurate results.

Moral of the story, if a vendor pitches you on “machine learning” it’s OK to be optimistic but be cautious. Here are some questions you can ask to see if the machine learning will make your team more productive:

  • How does the vendor help the algorithm focus on the important elements? How do they help their technology understand the data to reach the right conclusions?
  • How do they avoid a high rate of false positives? For example, if their machine learning algorithms find “an anomaly” what are the chances it’s a true positive?
  • How does the vendor make its alerts or findings actionable?

4 ways indeni machine learning differs from Splunk


Now that we are on the same page for machine learning, here are four ways that indeni differs from SIEM and Log Management solutions such as Splunk.

#1 indeni ingests configuration data in addition to statistics and logs of devices.

Collecting greater depths of information on devices and the software running on them allows indeni to identify issues with greater accuracy.

#2 indeni has the largest database of device knowledge.

indeni has a growing repository of known infrastructure issues and resolution steps for the largest Enterprises. This information is gathered from our customers, indeni engineers and third party experts around the globe. How does this help on a daily basis?

  • Root cause analysis: Instead of coming up with a hypothesis and then building a query in Splunk so that you can schedule alerts when the same log or event occurs, indeni has the knowledge built into its core alerting system, no scripting or queries required.
  • Troubleshooting: When you receive an alert in indeni, in addition to telling you the affected device or error code, indeni provides a human readable description, the implication of not addressing the event and steps to resolution, helping network and security operations teams prioritize focus areas and shorten the mean time to resolution.

#3 indeni connects admins and engineers across the globe

In addition to applying machine learning to the data in your environment, indeni learns from other indeni customers and applies those learnings to your indeni instance. Our users subscribe to a service called “indeni Insight,” which sends data from their environment to our central repository. The data is sanitized and contains general device characteristics and behavior information. For example what model the device is, what software is running on it, which features are enabled, the status of licenses or contracts, running metrics (CPU, memory, etc.), system logs, active users and much more. The result for administrators and engineers? It’s like leveraging the expertise of thousands of your IT operations friends at Fortune 500 companies.

#4 indeni’s algorithmic model is based on the assumption 99.9% of the time devices are working as expected.

Based on our experience as network and security professionals, we know a device malfunctions only 0.1% of the time. In addition, it is widely documented that 70% of network outages occur due to device misconfigurations. These two constraints are built into our machine learning algorithm which allow us to reduce false positives, saving our customers time and money.

At a glance: indeni vs. Splunk

SimilaritiesDifferences
  • indeni and Splunk ingest data from a variety of devices
  • indeni and Splunk machine learning are based on algorithmic models
  • indeni and Splunk machine learning correlate data
  • indeni ingests configuration data in addition to statistics and logs of network devices
  • indeni has the largest database of device knowledge
  • indeni connects admins and engineers with each other
  • indeni’s algorithmic model is based on the assumption that 99.9% of the time devices are working as expected. We help you find the .1%

Conclusion


indeni is capable of identifying specific issues, which pertain to specific types of products and even specific software builds, at a level of accuracy and actionability never seen before. With indeni customers can find health and operational issues before they happen in their infrastructure, proactively handle them and have a better life. Interested in trying indeni in your environment? Contact us or engage with one of our registered partners.