Back in 2016, I built an Internet-of-Thing (IoT) prototype system leveraging the “minimalist” design principle of the Actor model to simulate low-cost, low-powered IoT devices. A simplified version of the prototype was published in a previous blog post. The stripped-down application was written in Scala along with the Akka Actors run-time library, which is arguably the predominant Actor model implementation at present. Message Queue Telemetry Transport (MQTT) was used as the publish-subscribe messaging protocol for the simulated IoT devices. For simplicity, a single actor was used to simulate requests from a bunch of IoT devices.

In this blog post, I would like to share a version closer to the design of the full prototype system. With the same tech stack used in the previous application, it’s an expanded version (hence, II) that uses loosely-coupled lightweight actors to simulate individual IoT devices, each of which maintains its own internal state and handles bidirectional communications via non-blocking message passing. Using a distributed workers system adapted from a Lightbend template along with a persistence journal, the end product is an IoT system equipped with a scalable fault-tolerant data processing system.

Main components

Below is a diagram and a summary of the revised Scala application which consists of 3 main components:

1. IoT

• An IotManager actor which:
  • instantiates a specified number of devices upon start-up
  • subscribes to a MQTT pub-sub topic for the work requests
  • sends received work requests via ClusterClient to the master cluster
  • notifies Device actors upon receiving failure messages from Master actor
  • forwards work results to the corresponding devices upon receiving them from ResultProcessor
• Device actors each of which:
  • simulates a thermostat, lamp, or security alarm with random initial state and setting
  • maintains and updates internal state and setting upon receiving work results from IotManager
  • generates work requests and publishes them to the MQTT pub-sub topic
  • re-publishes requests upon receiving failure messages from IotManager
• A MQTT pub-sub broker and a MQTT client for communicating with the broker
• A configuration helper object, MqttConfig, consisting of:
  • MQTT pub-sub topic
  • URL for the MQTT broker
  • serialization methods to convert objects to byte arrays, and vice versa

2. Master Cluster

• A fault-tolerant decentralized cluster which:
  • manages a singleton actor instance among the cluster nodes (with a specified role)
  • delegates ClusterClientReceptionist on every node to answer external connection requests
  • provides fail-over of the singleton actor to the next-oldest node in the cluster
• A Master singleton actor which:
  • registers Workers and distributes work to available Workers
  • acknowledges work request reception with IotManager
  • publishes work results from Workers to ‘work-results’ topic via Akka distributed pub-sub
  • maintains work states using persistence journal
• A ResultProcessor actor in the master cluster which:
  • gets instantiated upon starting up the IoT system (more on this below)
  • consumes work results by subscribing to the ‘work-results’ topic
  • sends work results received from Master to IotManager

3. Workers

• An actor system of Workers each of which:
  • communicates via ClusterClient with the master cluster
  • registers with, pulls work from the Master actor
  • reports work status with the Master actor
  • instantiates a WorkProcessor actor to perform the actual work
• WorkProcessor actors each of which:
  • processes the work requests from its parent Worker
  • generates work results and send back to Worker

Master-worker system with a ‘pull’ model

While significant changes have been made to the IoT actor system, much of the setup for the Master/Worker actor systems and MQTT pub-sub messaging remains largely unchanged from the previous version:

• As separate independent actor systems, both the IoT and Worker systems communicate with the Master cluster via ClusterClient.
• Using a ‘pull’ model which generally performs better at scale, the Worker actors register with the Master cluster and pull work when available.
• Paho-Akka is used as the MQTT pub-sub messaging client.
• A helper object, MqttConfig, encapsulates a MQTT pub-sub topic and broker information along with serialization methods to handle MQTT messaging using a test Mosquitto broker.

What’s new?

Now, let’s look at the major changes in the revised application:

First of all, Lightbend’s Activator has been retired and Sbt is being used instead.

On persisting actors state, a Redis data store is used as the persistence journal. In the previous version the shared LevelDB journal is coupled with the first seed node which becomes a single point of failure. With the Redis persistence journal decoupled from a specific cluster node, fault tolerance steps up a notch.

As mentioned earlier in the post, one of the key changes to the previous application is the using of actors representing individual IoT devices each with its own state and capability of communicating with entities designated for interfacing with external actor systems. Actors, lightweight and loosely-coupled by design, serve as an excellent vehicle for modeling individual IoT devices. In addition, non-blocking message passing among actors provides an efficient and economical means for communication and logic control of the device state.

The IotManager actor is responsible for creating and managing a specified number of Device actors. Upon startup, the IoT manager instantiates individual Device actors of random device type (thermostat, lamp or security alarm). These devices are maintained in an internal registry regularly updated by the IoT manager.

Each of the Device actors starts up with a random state and setting. For instance, a thermostat device may start with an ON state and a temperature setting of 68F whereas a lamp device might have an initial state of OFF and brightness setting of 2. Once instantiated, a Device actor will maintain its internal operational state and setting from then on and will report and update the state and setting per request.

Work and WorkResult

In this application, a Work object represents a request sent by a specific Device actor and carries the Device’s Id and its current state and setting data. A WorkResult object, on the other hand, represents a returned request for the Device actor to update its state and setting stored within the object.

Responsible for processing the WorkResult generated by the Worker actors, the ResultProcessor actor simulates the processing of work result – in this case it simply sends via the actorSelection method the work result back to the original Device actor through IotManager. Interacting with only the Master cluster system as a cluster client, the Worker actors have no knowledge of the ResultProcessor actor. ResultProcessor receives the work result through subscribing to the Akka distributed pub-sub topic which the Master is the publisher.

While a participant of the Master cluster actor system, the ResultProcessor actor gets instantiated when the IoT actor system starts up. The decoupling of ResultProcessor instantiation from the Master cluster ensures that no excessive ResultProcessor instances get started when multiple Master cluster nodes start up.

Test running the application

Complete source code of the application is available at GitHub.

To run the application on a single JVM, just git-clone the repo, run the following command at a command line terminal and observe the console output:

The optional NumOfDevices parameter defaults to 20.

To run the application on separate JVMs, git-clone the repo to a local disk, open up separate command line terminals and launch the different components on separate terminals:

Sample console log

Below is filtered console log output from the console tracing the evolving state and setting of a thermostat device:

The following annotated console log showcases fault-tolerance of the master cluster – how it fails over to the 2nd node upon detecting that the 1st node crashes:

Scaling for production

The Actor model is well suited for building scalable distributed systems. While the application has an underlying architecture that emphasizes on scalability, it would require further effort in the following areas to make it production ready:

• IotManager uses the ‘ask’ method for message receipt confirmation via a Future return by the Master. If business logic allows, using the fire-and-forget ‘tell’ method will be significantly more efficient especially at scale.
• The MQTT broker used in the application is a test broker provided by Mosquitto. A production version of the broker should be installed preferably local to the the IoT system. MQTT brokers from other vendors like HiveMQ, RabbitMQ are also available.
• As displayed in the console log when running the application, Akka’s default Java serializer isn’t best known for its efficiency. Other serializers such as Kryo, Protocol Buffers should be considered.
• The Redis data store for actor state persistence should be configured for production environment

Further code changes to be considered

A couple of changes to the current application might be worth considering:

Device types are currently represented as strings, and code logic for device type-specific states and settings is repeated during instantiation of devices and processing of work requests. Such logic could be encapsulated within classes defined for individual device types. The payload would probably be larger as a consequence, but it might be worth for better code maintainability especially if there are many device types.

Another change to be considered is that Work and WorkResult could be generalized into a single class. Conversely, they could be further differentiated in accordance with specific business needs. A slightly more extensive change would be to retire ResultProcessor altogether and let Worker actors process WorkResult as well.

State mutation in Akka Actors

In this application, a few actors maintain mutable internal states using private variables (private var):

• Master
• IotManager
• Device

As an actor by-design will never be accessed by multiple threads, it’s generally safe enough to use ‘private var’ to store changed states. But if one prefers state transitioning (as opposed to updating), Akka Actors provides a method to hot-swap an actor’s internal state.

Hot-swapping an actor’s state

Below is a sample snippet that illustrates how hot-swapping mimics a state machine without having to use any mutable variable for maintaining the actor state:

Simplified for illustration, the above snippet depicts a Worker actor that pulls work from the Master cluster. The context.become method allows the actor to switch its internal state at run-time like a state machine. As shown in the simplified code, it takes an ‘Actor.Receive’ (which is a partial function) that implements a new message handler. Under the hood, Akka manages the hot-swapping via a stack. As a side note, according to the relevant source code, the stack for hot-swapping actor behavior is, ironically, a mutable ‘private var’ of List[Actor.Receive].

Recursive transformation of immutable parameter

Another functional approach to mutating actor state is via recursive transformation of an immutable parameter. As an example, we can avoid using a mutable ‘private var registry’ as shown in the following ActorManager actor and use ‘context.become’ to recursively transform a registry as an immutable parameter passed to the updateState method:

IoT (Internet of Things) has recently been one of the most popular buzzwords. Despite being over-hyped, we’re indeed heading towards a foreseeable world in which all sorts of things are inter-connected. Before IoT became a hot acronym, I was heavily involved in building a Home-Area-Network SaaS platform over the course of 5 years in a previous startup I cofounded, so it’s no stranger to me.

At the low-level device network layer, there used to be platform service companies providing gateway hardware along with proprietary APIs for IoT devices running on sensor network protocols (such as ZigBee, Z-Wave). The landscape has been evolving over the past couple of years. As more and more companies begin to throw their weight behind building products in the IoT ecosystem, open standards for device connectivity emerge. One of them is MQTT (Message Queue Telemetry Transport).

Message Queue Telemetry Transport

MQTT had been relatively little-known until it was standardized at OASIS a couple of years ago. The lightweight publish-subscribe messaging protocol, MQTT, has since been increasingly adopted by major players, including Amazon, as the underlying connectivity protocols for IoT devices. It’s TCP/IP based but its variant, MQTT-SN (MQTT for Sensor Networks), covers sensor network communication protocols such as ZigBee. There are also quite a few MQTT message brokers, including HiveMQ, Mosquitto and RabbitMQ.

IoT makes a great use case for Akka actor systems which come with lightweight loosely-coupled actors in decentralized clusters with robust routing, sharding and pub-sub features, as mentioned in a previous blog post. The actor model can be rather easily structured to emulate the operations of a typical IoT network that scales in device volume. In addition, availability of MQTT clients for Akka such as Paho-Akka makes it easy to communicate with MQTT brokers.

A Scala-based IoT application

UPDATE: An expanded version of this application with individual actors representing each of the IoT devices, each of which maintains its own internal state and setting, is now available. Please see the Akka Actors IoT v.2 blog post for details.

In this blog post, I’m going to illustrate how to build a scalable distributed worker system using Akka actors to service requests from a MQTT-based IoT system. A good portion of the Akka clustering setup is derived from Lightbend’s Akka distributed workers template. Below is a diagram of the application:

As shown in the diagram, the application consists of the following components:

1. IoT

• A DeviceRequest actor which:
  • simulates work requests from IoT devices
  • publishes requests to a MQTT pub-sub topic
  • re-publishes requests upon receiving failure messages from a topic subscriber
• An IotAgent actor which:
  • subscribes to the mqtt-topic for the work requests
  • sends received work requests via ClusterClient to the master cluster
  • sends DeviceRequest actor a failure message upon receiving failure messages from Master actor
• A MQTT pub-sub client, MqttPubSub, for communicating with a MQTT broker
• A configuration helper object, MqttConfig, consisting of:
  • MQTT pub-sub topic
  • URL for the MQTT broker
  • Serialization methods to convert objects to byte arrays, and vice versa

2. Master Cluster

• A fault-tolerant decentralized cluster which:
  • manages a singleton actor instance among the cluster nodes (with a specified role)
  • delegates ClusterClientReceptionist on every node to answer external connection requests
  • provides fail-over of the singleton actor to the next-oldest node in the cluster
• A Master singleton actor which:
  • registers Workers and distributes work to available Workers
  • acknowledges work request reception with IotAgent
  • publishes work results to a work-results topic via Akka distributed pub-sub
  • maintains work states using persistence journal
• A PostProcessor actor in the master cluster which:
  • simulates post-processing of the work results
  • subscribes to the work-results topic

3. Workers

• An actor system of Workers each of which:
  • communicates via ClusterClient with the master cluster
  • registers with, pulls work from the Master actor
  • reports work status with the Master actor
  • instantiates a WorkProcessor actor to perform the actual work
• WorkProcessor actors which process the work requests

Source code is available at GitHub.

A few notes:

1. Neither IotAgent nor Worker actor system is a part of the master cluster, hence both need to communicate with the Master via ClusterClient.
2. Rather than having the Master actor spawn child Workers and push work over, the Workers are set up to register with the Master and pull work from it – a model similar to what Derek Wyatt advocated in his post.
3. Paho-Akka is used as the MQTT pub-sub client with configuration information held within the helper object, MqttConfig.
4. The helper object MqttConfig consists of MQTT pub-sub topic/broker information and methods to serialize/deserialize the Work objects which, in turn, contains Device objects. The explicit serializations are necessary since multiple JVMs will be at play if one launches the master cluster, IoT and worker actor systems on separate JVMs.
5. The test Mosquitto broker at tcp://test.mosquitto.org:1883 serves as the MQTT broker. An alternative is to install a MQTT broker (Mosquitto, HiveMQ, etc) local to the IoT network.
6. The IotAgent uses Actor’s ask method (?), instead of the fire-and-forget tell method (!), to confirm message receipt by the Master via a Future return. If the receipt confirmation is not so important, using the tell method will be a much preferred choice for performance.
7. This is primarily a proof-of-concept application of IoT using Akka actors, hence code performance optimization isn’t a priority. In addition, for production systems, a production-grade persistence journal (e.g. Redis, Cassandra) should be used and multiple-Master via sharding could be considered.

Test-running

Similar to how you would test-run Lightbend’s distributed workers template, you may open up separate command line terminals and run the different components on separate JVMs, adding and killing the launched components to observe how the systems scale out, fail over, persist work states, etc. Here’s an example of test-run sequence:

Below are some sample console output.

Console Output: Master seed node with persistence journal:

Console Output: IotAgent-DeviceRequest node:

Console Output: Worker node: