MIMIC MQTT Lab: Test MQTT 5 support on AWS IoT Core

 AWS recently announced MQTT 5 support for AWS IoT.

We tested it in less than 5 minutes with MIMIC MQTT Lab AWS . You can do the same to make sure your
AWS IoT application uses the latest MQTT 5 features such as properties in PUBLISH messages, etc. 

Check the 2-minute Youtube video that shows the MQTT 5 CONNACK with new reason code:

CONNACK rc=0x00 Session Expiry Interval 0,Receive Maximum 100,Maximum QoS 1,Retain Available 1,Maximum Packet Size 149504,Topic Alias Maximum 8,Wildcard Subscription Available 1,Subscription Identifiers Available 0,Shared Subscription Available 1,Server Keep Alive 50

When we connect with the disallowed QOS 2, we get a new self-explanatory error code:

CONNACK rc=0x9b Reason String CONNACK:QOS 2 is not supported:861b3462-65d8-ba70-5472-63869294a5a1

and when we send a malformed PUBLISH (empty topic and topic alias):

INFO  12/02.10:53:07 - MQTT[AGT=3916] - sent CONNECT (51 bytes)
INFO  12/02.10:53:07 - MQTT[AGT=3916] - rcvd CONNACK rc=0x00 Session Expiry Interval 0,Receive Maximum 100,Maximum QoS 1,Retain Available 1,Maximum Packet Size 149504,Topic Alias Maximum 8,Wildcard Subscription Available 1,Subscription Identifiers Available 0,Shared Subscription Available 1,Server Keep Alive 50
INFO  12/02.10:53:08 - MQTT[AGT=3916] - sent PUBLISH (126 bytes)
INFO  12/02.10:53:08 - MQTT[AGT=3916] - rcvd DISCONNECT reason 0x82 (Reason String DISCONNECT:Data in packet does not conform to MQTT specification:19ec6dc1-0b50-888c-6c3e-3be26faee968)

MQTT performance testing - Best Practices

The MIMIC Simulator performance testing methodology attempts to overcome
common problems with published performance benchmarks, specially in the 

IoT arena. In this article we examine one recently published report and discuss 

how to make it better.

 

The main problem with any performance test is that the results apply only to the 

specific test scenario. If the test scenario is carefully selected, the results will be 

relevant for a wide variety of situations. If the test report is good, then the exact 

methodology is documented, so you can evaluate it, and determine whether the 

results can be useful for you. For example this report

https://www.researchgate.net/publication/354610718_Stress-Testing_MQTT_Brokers_A_Comparative_Analysis_of_Performance_Measurements

performed one test scenario for an uncommon situation of a small set (3) of high-

frequency publishers, and 15 mosquitto_sub subscribers. Plain text MQTT is only 

used in trivial situations, and there is no indication that TLS transport is measured.

Latency measurements suffer from the time synchronization problem on different 

systems.

 

Specifically, it says right at the beginning in the abstract

 

"The evaluation of the brokers is performed by a realistic test scenario"

 but then, in section 4.1.1. Evaluation Conditions:

"

Number of topics:                   3
                                    (via 3 publisher threads)
Number of publishers:               3
Number of subscribers:              15 (subscribing to all 3 topics)
Payload:                            64 bytes
Topic names used to publish large
number of messages:                 ‘topic/0’, ‘topic/1’, ‘topic/2’
Topic used to calculate latency:    ‘topic/latency

"

 

so rather than testing a large-scale environment, a small set (3) of high-frequency 

publishers, and 15 mosquitto_sub subscribers was used. In our experience, no 

recent broker has any problem with less than 1000 publishers.

Second, in section 4. the subscriber back-end is detailed:

"The subscriber machine used the “mosquitto_sub” command line
subscribers, which is an MQTT client for sub- scribing to topics and
printing the received messages. During this empirical evaluation, the
“mosquitto_sub” output was redirected to the null device (/dev/null) "

using a the simple mosquitto_sub client which is single threaded. In addition,

the subscribers subscribe to all topics, probably the wildcard topic #. So, out of 

many code paths in the broker, the least commonly used is tested. If your 

application uses a topic hierarchy, with different subscribers subscribing to 

different topic trees, then topic matching performance needs to be exercised.

Third, while QOS 0, 1 and 2 seem to be tested, only a single payload size 

was used, and there is no indication that TLS transport is measured.

Fourth, they attempt to measure latency correctly, ie. section 4.1.2

"Latency is defined as the time taken by a system to transmit a message
from a publisher to a subscriber"

but their methodology is flawed, since it is almost impossible to synchronize the 

clocks on 2 separate systems to millisecond accuracy and in table 6 the latencies 

are in the 1ms range. So, the measurements rely on unknown synchronization. 

For an example of the MIMIC latency testing methodology see this blog post.

Migrating from shuttered IBM Watson IoT platform

In a previous article simulation was recognized as helping prevent IoT project failures
so prevalent in the industry.

 

With the recent announcements of the shuttering of the Google IoT Core and 

IBM Watson IoT platforms, we can suggest that MIMIC IoT Simulator can be used 

to help migrate from the obsolete IoT platforms to a new offering by:

1) running a facsimile of your environment in MIMIC

2) staging migration to the new platform

3) testing requirements at various scales to make it future-proof

before you impact your production network.

How to scale your MQTT lab to 1000 sensors in minutes

TL;DR Money saved: $40,000. Time saved: immeasurable.

We needed to create a MQTT lab with 1000 sensors to test a subscriber client with

realistic telemetry. The open-source client tracks any key value, and alerts if any

arbitrarily pre-selected value exceeds a threshold.

We bought 1 real Shelly Plus H&T sensor for $40.

After you have configured it, it sends MQTT messages to the broker, but only 

every time the temperature and humidity changes. So, to test our application, we 

would have had to run to the refridgerator quite often to make it change the 

temperature.

As you can see from the screenshot

it sends JSON payloads, but very infrequently. In our case, after 6 minutes

So, every time we wanted a message, we needed to change the temperature.

To accelerate development, we used MIMIC.

First we just captured the messages with wireshark, recorded into MIMIC MQTT Simulator, 

and generated messages whenever and however we wanted. Rather than waiting for minutes, we can

send any message with any value in seconds, speeding up development time. Then we multiplied

the sensor 1000-fold, quickly reaching the required scalability at no additional cost.

This video shows the process in 2 minutes:

Money saved: $40,000. Time saved: immeasurable.

IoT platform demo requirements

IoT platform software is complicated leading to project delays and frequent failures. To 

select the right platform for their needs, the customer has to learn and evaluate features such 

as data acquisition, analytics, graphing, database integration, alarming, etc.

Product evaluation is usually done in a couple of steps, starting with a survey leading to practical 

hands-on trial, each further filtering from the wide assortment of products. The initial evaluation needs 

to whittle down to a couple of candidates for deeper investigation before a proof-of-concept effort. 

Free brochures, videos, and other static marketing collateral can only go so far to get a customer

interested in the IoT platform software. What is needed is a more engaging, interactive format.

Thus, in order to best advertise platform features, an initial product demo has to be

1. free - to get the foot in the door, you should not be charged. Further, the customer
   should not need to give a credit card number, or any kind of personal information
2. immediate - the customer should get to the demo in a couple of clicks, and the instructions
   should not consist of more than a couple of lines. In total, the demo should be done in a
   couple of minutes.
3. always on - in a global economy, the demos should be accessible 24 hours a day, 7 days
   a week.
4. interactive - the demos should go beyond replay of videos, and consist of live interactions
   for each customer.
5. dynamic - the customer should be able to view the features you want to show off in the
   most compelling way and be most realistic.
6. predictable - the demo should behave the same way each time they visit, so that you can
   control it, describe it, and it matches the customer's expectations.
7. diverse - every customer scenario is unique, requiring different platform features and
   types of sensor hardware. The demos should showcase the various features that the customer wants.
8. scalable - scaling up an IoT application is the hardest part. The demo should prove
   how the platform enables large scale applications.
   

For examples of demos with MIMIC IoT Simulator that we have provided to our partners 

• Cumulocity
• Losant
• Ubidots 
• TagoIO
   

see also this blog post .

MIMIC SNMP Simulator and neteXpose DNA

MIMIC SNMP Simulator can simulate details of each device to the finest level. In 

this integration, netExpose DNA discovered the chassis details of a simulated 

Alcatel switch, allowing them to test their management application with all sorts

of device scenarios.

Migrating to Google IoT Core alternatives

If you are contemplating migrating from Google IoT before it retires in a year, you 

might want to use MIMIC MQTT Simulator and its SaaS offering for Google IoT 

https://mqttlab.iotsim.io/gcp to design / develop / test your migration strategy.

 

MIMIC MQTT Simulator allows you to simulate a large number of your assets that 

connect to Google IoT Core, then migrate them to the alternative of your choice.

You can duplicate your current IoT environment in MIMIC to make sure the new 

IoT platform handles your specific requirements.

MIMIC MQTT Lab for Azure IoT Hub - get started in 5 minutes

Available for immediate use is MIMIC MQTT Lab Azure to get started with Azure IoT Hub

in minutes, as shown in this 3-minute Youtube video

Start with #nocode, #loweffort, #lowcost, then graduate to more advanced IoT simulation

to develop, test, prototype, demo and train your Azure IoT application.

Generate predictable, customized telemetry graphed in Azure Time Series Insight in under

2 minutes