A model-based infrastructure for the specification and runtime execution of self-adaptive IoT architectures

Abstract

To meet increasingly restrictive requirements and improve quality of service (QoS), Internet of Things (IoT) systems have embraced multi-layered architectures leveraging edge and fog computing. However, the dynamic and changing IoT environment can impact QoS due to unexpected events. Therefore, proactive evolution and adaptation of the IoT system becomes a necessity and concern. In this paper, we present a model-based approach for the specification and execution of self-adaptive multi-layered IoT systems. Our proposal comprises the design of a domain-specific language (DSL) for the specification of such architectures, and a runtime framework to support the system behaviuor and its self-adaptation at runtime. The code for the deployment of the IoT system and the execution of the runtime framework is automatically produced by our prototype code generator. Moreover, we also show and validate the extensibility of such DSL by applying it to the domain of underground mining. The complete infrastructure (modeling tool, generator and runtime components) is available in a online open source repository.

1 Introduction

Nowadays, billions of connected devices sense, communicate, and share information about their environment. To manage all these devices, traditional Internet of Things (IoT) systems rely on cloud-based architectures, which allocate all processing and storage capabilities to cloud servers. Although cloud-based IoT architectures have advantages such as reduced maintenance costs and application development efforts, they also have limitations in bandwidth and communication delays [1]. Given these limitations, edge and fog computing have emerged with the goal of distributing processing and storage closer to data sources (i.e. things). As a result, new IoT systems aim to leverage the advantages of edge, fog, and cloud computing by following a multi-layered architecture.

Nevertheless, creating such complex designs is a challenging task. Even more challenging is managing and adapting IoT systems at runtime to ensure the optimal performance of the system while facing changes in the environmental conditions. Indeed, IoT systems are commonly exposed to changing environments that induce unexpected events at runtime (such as unstable signal strength, latency growth, and software failures) that can impact its Quality of Service (QoS). To deal with such events, a number of runtime adaptation rules should be automatically applied, e.g. architectural adaptations such as auto-scaling and offloading tasks.

In this sense, a better support to define and execute complex IoT systems and their (self)adaptation rules to semi-automate the deployment and evolution process is necessary [2]. A usual strategy when it comes to modeling complex domains is to develop a domain-specific language (DSL) for that domain [3]. In short, a DSL offers a set of abstractions and vocabulary closer to the one already employed by domain experts. Nevertheless, current IoT modeling approaches do not typically cover multi-layered architectures [4,5,6,7] and even less include a sublanguage to ease the definition of the dynamic rules governing the IoT system. Our research aims at overcoming this situation by presenting a model-based infrastructure for the specification and runtime execution of multi-layered IoT architectures, including self-adaptation rules. Our proposal combines a DSL for the specification of static and dynamic aspects of this type of systems together with a runtime infrastructure and a code-generator able to semi-automate their deployment and runtime monitoring and adaptation.

This work is an extension of our study presented in [8], in which we proposed a first version of a DSL for IoT systems and a proof of concept of a code generator. The current work extends this previous contribution to the following aspects:

• Metamodel improvement: we have enhanced the metamodel to support modeling new DSL concepts such as sensor threshold values, publish/subscribe messaging and data persistence for containers.

• Runtime support: we have developed a framework based on the MAPE-K [9] loop to automatically monitor, execute the expected behaviour and self-adapt the IoT system by executing the IoT execution rules modeled with the DSL, including rules for the self-adaptation of the system to ensure its QoS.

• Code generator enhancements: We now generate the code required to support the execution of the whole system at runtime (including the code for infrastructure monitoring and system management tools).

• a DSL extension for the mining industry: we propose an extension of our DSL focused on the modeling and operation of IoT systems in the underground mining industry, highlighting the ability to reuse the DSL in other domains.

• Empirical evaluations: we have designed and conducted empirical experiments to validate the expressiveness and usability of our DSL and the correctness of the generated code.

The remainder of the paper is organized as follows: Section 2 presents a running example to illustrate our approach. Section 3 introduces the DSL to specify multi-layered IoT systems and adaptation rules. Section 4 present an extension of our DSL for IoT systems deployed in underground mining. In Sect. 5, we present the DSL implementation, the framework to support IoT systems at runtime, and code generation. In Sect. 6, we validate the usability of our DSL and the generated runtime. Finally, the related work to this research is summarized in Sect. 7, and the conclusions and future work are presented in Sect. 8.

2 Running example

We will use a Smart Building scenario as a running example to illustrate our approach. Smart buildings seek to optimize different aspects such as ventilation, heating, lighting, energy efficiency, etc [10].

For the purposes of our example, let’s assume that a hotel company (Hotel Beach) wants to reduce fire risks by automating disaster management in its hotels. A fire alarm and monitoring system are implemented in each of the company’s hotels. We will assume that all buildings (hotels) have three floors with two rooms each. Fig. 1 presents an overview of the 1st floor. Based on this, the infrastructure (device, edge, and cloud layers) of the company hotel IoT system would be as follows:

• Device layer Each room has a temperature sensor, a carbon monoxide (CO) gas sensor, and a fire water valve. Furthermore, an alarm is deployed on the lobby. Each sensor has a threshold measurement to activate the corresponding alarm, e.g., a person should not be continuously exposed to CO gas level of 50 parts per million (ppm) for more than 8 hours.

• Edge layer In each room, an edge node receives the information collected by the sensors of the device layer and run a software container (C1 and C2) for analyzing sensor data in real time to check for the presence of smoke and generate an alarm state that activates the actuators. A fog node (linked to the edge nodes), located in this same floor, runs the C3 container (running App2, a machine learning model to predict fires), and C4 (running App3, in charge of receiving and distributing data, typically a Message Queuing Telemetry Transport (MQTT).

• Cloud layer The cloud layer has a cloud server node that runs the C5 container, a web application (App4) to display historical information of sensor data and of fire incidents in any of the hotel’s property of the company.

Fig. 1

Overview of the smart building IoT system, first floor

This covers the static view of the system. But in any IoT system, there are critical applications that should be available all the time. For example, the availability of the containers running App1 (real-time smoke monitoring) should be guaranteed. However, some environmental factors can impact their availability. In these cases, the IoT system must self-adapt to guarantee its operation. For instance, a flooding could cause failures in the edge-a1 node; then it will be necessary to migrate the C1 container to another suitable node to ensure the continuous monitoring of the smoke presence.

Our research addresses this type of architectural adaptations by proposing a rule-based language for the runtime execution of IoT systems that can also be used to address their functional requirements. An example of this latter case would be a rule stating that when one of the room sensors detects CO gas greater than 400 ppm, an of the alarms should be triggered. The next section shows how we can model all these concepts.

3 A DSL for the specification of multi-layered IoT systems

A DSL is defined by three core ingredients [3]: the abstract syntax (i.e., the concepts of the DSL and their relationships), the concrete syntax (the notation to represent those concepts), and its semantics which are hardly ever formalized but based on the shared understanding of the domain. In this chapter, we present our DSL for modeling multi-layered IoT architectures (Sect. 3.1) and their dynamic (3.2) rules.

3.1 Modeling the IoT architecture

Traditionally, IoT system architectures consisted of two layers: device and cloud [11]. The device layer is composed by sensors (devices that sense physical properties such as temperature and humidity), actuators (devices such as valves, fans, or alarms that can influence an automated process), and network devices that send data to the cloud layer, composed of servers that run the application logic. Today, multi-layered architectures based on edge and fog computing have emerged to increase the flexibility of pure cloud deployments and help meet non-functional IoT system requirements [12].

There are several definitions for edge and fog computing. [13] states that edge computing enables computations to be performed at the edge, supporting cloud and IoT services. On the other hand, [14] defines fog computing as a bridge between the cloud and the edge of the network to facilitate the deployment of new IoT applications providing computation, storage, and network services. There are strong similarities between these two concepts, but the main difference is where the processing takes place. While fog computing take place on LAN-connected nodes usually close to end user devices, edge computing takes place on personal devices directly connected to sensors and actuators, or on gateways physically close to these [14, 15].

Edge and fog computing can leverage containerization as a virtualization technology [16]. Containers, compared to virtual machines, are lightweight, simple to deploy, support multiple architectures, have a short start-up time, and are suitable for dealing with the heterogeneity of edge and fog nodes. In terms of communications, asynchronous message-based architectures are typically chosen, especially for IoT systems that require high scalability [17]. The publisher/subscriber pattern and the MQTT protocol are becoming the standard for Machine-to-Machine communications [18], where messages are sent (by publishers) to a message broker server and routed to destination clients (subscribers).

Our DSL enables the specification of all these concepts.

3.1.1 Abstract syntax

The abstract syntax of a DSL is commonly defined through a metamodel that represents the domain concepts and their relationships. Fig. 2 shows our metamodel that abstracts the concepts required to define multi-layered IoT architectures.

The sensors and actuators of the device layer are modeled using the Sensor and Actuator concepts that inherit from the IoTDevice concept. All IoTDevices have a connectivity type (such as Ethernet, Wi-Fi, ZigBee, or another). The location of IoTDevices can be specified through geographic coordinates (latitude and longitude attributes). Moreover, Sensors and Actuators have a type represented by the concepts SensorType and ActuatorType to organize different sensors belonging to the same category and be able to define global rules for them. For instance, following the running example (Fig. 1), there are temperature and smoke type sensors, and there are valve and alarm type actuators. We also cover the concepts for specifying MQTT communications: IoTDevices are publishers or subscribers to a topic specified by the relationship to the Topic) concept. The gateway of an IoTDevice can be modeled through the gateway relationship with the EdgeNode concept. Via this gateway, the sensor can communicate with other nodes, e.g., the MQTT broker node. Additionally, the threshold value and unit of the monitored variable by a sensor can be represented through the attributes threshold and unit.

Physical (or even virtual) spaces such as rooms, stairs, buildings, or tunnels can be represented by the concept Region. A Region can contain subregions (relationship subregions in the metamodel). For example, region Floor1 (Fig. 1) contains subregions Room1, Room2, Lobby, and Stairs. IoTDevices, EdgeNodes, and FogNodes are deployed and are located in a region or subregion (represented by region relationships in the metamodel). Back to the running example, the edge-a1 node is located in the RoomA1 region of Floor1 of the Hotel Beach, while the fog-f1 node is located in the Lobby region of Floor1.

Edge, fog and cloud nodes are all instances of Node, one of the key concepts of the metamodel. Communication between nodes can be specified via the linkedNodes relationship, as we may want to indicate what nodes on a certain layer could act as reference nodes in another layer. Nodes can also be grouped in clusters that work together. A Node can host several software containers according to its capabilities and resources (primarily cpuCores, memory, and storage). The CPU and memory usage of a container can be restricted through cpuLimit and memoryLimit attributes. Each software container runs an application (represented by the concept Application) that has a minimum of required resources specified by the attributes cpuRequired and memmoryRequired. The repository of the application image is specified through the imageRepo attribute. The container volumes and their paths (a mechanism for persisting data used and generated by containers) are represented by the Volume concept.

Fig. 2

Multi-layer IoT architecture metamodel

3.1.2 Concrete syntax

The concrete syntax refers to the type of notation (such as textual, or graphical) to represent the concepts of the metamodel. Graphical DSLs involve the development of models using graphic items such as blocks, arrows, axes, and so on. Textual DSLs involve modeling using configuration files and text notes. Though most DSLs employ a single type of notation, they could benefit from offering several alternative notations, each one targeting a different type of DSL user profile. This is the approach we follow here, leveraging the benefits of using a projectional editor.

Projectional editors enable the user’s editing actions to directly change the Abstract Syntax Tree (AST) without using a parser [19]. That is, while the editing experience simulates that of classical parsing-based editors, there is a single representation of the model stored as an AST and rendered in a variety of perspectives thanks to the corresponding projectional editors that can deal with mixed-language code and support various notations such as tables, mathematical formulas, or diagrams.

Indeed, we take advantage of JetBrains Meta Programming Systems (MPS) projectional editors to define a set of complementary notations for the metamodel concepts. We blend textual, tabular, and tree view, depending on the element to be modeled. We next employ these notations to model our running example (Sect. 2). More technical details on the implementation of our concrete notations are presented in Sect. 5.

3.1.3 Example scenario

We present next how to model the IoT architecture of the running example (Sect. 2) using our DSL. First, Fig. 3a depicts the modeling of the application App1, including its technical requirements and repository address. Then, Fig. 3b shows the specification of the Hotel Beach regions, in particular those on Floor 1 (four subregions: two Rooms, the Lobby and the Stairs).

Fig. 3

Application and Regions modeling example

Additionally, IoT devices can be modeled using a tabular notation. Fig. 4 shows the list of sensors and actuators located in the RoomA1 region (we have omitted some parameters for readability purposes. To specify the nodes of the system architecture, we propose a tabular notation as well (Fig. 5 shows edge-a1 and fog-f1 node modeling). The node description includes the layer it belongs to, the hardware properties, the regions where it is located, and the hosted application containers. Each container includes the specification of the application to be executed, limits, and volumes (e.g., the volume of the C4 container for the configuration of the MQTT broker).

Fig. 4

IoT devices modeling example (tabular notation)

Fig. 5

Nodes and containers modeling example (tabular notation)

3.2 Modeling dynamic rules

The dynamic environment of an IoT system requires dealing with expected an unexpected events. The former may trigger actions to comply with the standard behaviour of the system (e.g. to turn on an alarm upon detection of fire), unexpected ones may require a self-adaptation of the system itself to continue its normal operation. This section presents a rule-based language that can cover both types of events (and even mix them in a single rule). This facilitates an homogeneous of all the dynamic aspects of an IoT system.

To decide what unexpected environmental situation should we include and what the standard patterns of response are common in the self-adaptation of IoT systems, we rely on our previous systematic literature review [11]. For instance, the three architectural adaptations (offloading, scaling, and redeployment) addressed in this study were identified in the SLR. Our language covers all of them and even enables complex rules where policies involving several strategies can be attempted in a given order.

3.2.1 Abstract syntax

The metamodel representing the abstract syntax for defining the rules is depicted in Fig. 6.

Every rule is an instance of Rule that has a triggering condition, which is an expression. We reuse an existing Expression sublanguage to avoid redefining in our language all the primitive data types and all the basic arithmetic and logic operations to manipulate them. Such Expression language could be, for instance, the Object Constraint Language (OCL), but to facilitate the implementation of the DSL later on, we directly reused the MPS Baselanguage. The metamodel extends the generic Expression concept by adding sensor and QoS conditions that can be combined also with all other types of expressions (e.g., BinaryOperations, Literals, or BooleanConstants) in a complex conditional expression.

A SensorCondition represents the occurrence of an event resulting from the analysis of sensor data (e.g., the detection of dioxide carbon gas by the gas-a1 sensor). Note that SensorCondition conditions can be linked to specific sensors or to sensor types to express conditions involving a group of sensors.

Similarly, the QoSCondition is a relational expression that represents a threshold of resource consumption or QoS metrics. This condition allows checking a Metric (such as Latency, CPU consumption, and others) on a specific node or a group of nodes belonging to a Region or Cluster. For example, the condition $cpu(HotelBeach -> edge\_nodes) > 50\%$ is triggered when the CPU consumption on the edge nodes of the Hotel exceeds 50%.

Moreover, we can define that the condition should be true over a certain period (to avoid firing the rule in reaction to minor disturbances) before executing the rule. Once fired, all or some of the actions are executed in order, depending on the allActions attribute. If set to false, only the number of Actions specified by the attribute actionsQuantity must be executed, starting with the first one in order and continuing until the required number of actions have been successfully applied.

For the sake of clarity, we have grouped the rule concepts into two categories: Architectural Adaptation Rules and Functional Rules but note that they could be all combined, e.g., a sensor event could trigger at the same time a functional response such as triggering an alarm and, at the same time, an automatic self-adaptation action, such as scaling of apps related to the event to make sure the IoT system has the capacity to collect more relevant data).

Among the self-adaptation patterns, the Offloading action consists in migrating a container from a source node to a destination node. This migration can be among nodes of different layers. The container relationship represents the container that will be offloaded. The target node is specified by the targetNode relationship. However, if the target node does not have the resources to host the container, a cluster or a group of nodes in a Region can be specified (targetRegion and targetCluster relationships) to offload the container. The Scaling action involves deploying replicas of an application. This application is represented by the app relationship, and the number of replicas to be deployed is defined by the instances attribute. The replicas of the application are deployed in one or several nodes of the system represented by the targetNodes, targetCluster, and targetRegion relationships. The Redeployment action consists in stopping and redeploying a container running on a node. The container to redeploy is indicated by the container relationship. Finally, the OperateActuator action is to control the actuators of the system (e.g., to activate or deactivate an alarm). The message attribute represents the message that will be published in the broker and interpreted by the actuator.

Fig. 6

IoT system dynamic rules metamodel

3.2.2 Concrete syntax

Rules are specified thanks to a textual notation using as keywords the names of the metaclasses of the abstract syntax. The conditions follow the grammar of a relational expression with the use of mathematical symbols (such as <, >, and $=$) and logical operators (such as & and $\parallel$). The rule editor (see Sect. 5) offers a powerful autocomplete feature to guide the designer through the rule creation process.

3.2.3 Example scenario

We show how to use the rule’s concrete syntax to model two example rules from the smart building example.

First, to guarantee the execution of the C4 container deployed on the fog-f1 node, we modeled the rule as shown in (Fig. 7a). This rule offloads the container C4 hosted on node fog-f1 to a nearby node (e.g., node edge-b1) when the CPU consumption exceeds 80% for one minute. If the edge-b1 node does not have the necessary resources to host that new container (when the rule is activated), a Region (e.g., Floor1) can be specified so that a suitable node will be searched there. However, if this offloading action cannot be executed, for example, because in Floor1 there is no node capable of hosting the container, then we must define a backup action. Therefore, we have modeled a second action (Scaling) to deploy a new container instance of the App3 application on any of the nodes of the Hotel Beach. When a list of actions is specified, the checkbox all actions controls whether all or only a certain number of them should be performed. All actions in the list will be performed. For Rule 1, only one action (the first one, or the second one if the first one fails) will be executed.

Secondly, we model another rule (see Fig. 7b) to activate the alarm (a-lobby) when any gas sensors in the Floor1 region (gas-a1 or gas-b1) detects a gas concentration greater than 400ppm for 10 seconds. The “On” message is published in the broker topic consumed by the actuator (a-lobby alarm). Note that there are two ways to model this rule. While Option 1 involves all CO type sensors on Floor 1, Option 2 directly involves both gas sensors.

Fig. 7

Example of rule modeling

4 DSL extension: coal underground mining

Our DSL can be used as is to model any type of multi-layered IoT system. However, it has also been designed to be easily extensible to further tailor it to specific types of IoT systems. As an example, we present an extension of our DSL to model underground mining systems as this is a key economic sector in the local region of one of the authors and there is a need for a better modeling of these systems, e.g., for analysis of regulatory compliance.

Indeed, the dynamic and hostile environment of the underground mining industry threatens the operation of IoT systems (e.g. by causing physical damage to the devices) implemented primarily to monitor and ensure the safety of workers. Explosive and toxic gases, risk of geotechnical failure, fire, high temperatures and humidity are some of the risks. Therefore, mining IoT systems must cope with these unexpected changes by self-adapting to guarantee a proper run-time operation.

In addition to system self-adaptations, our DSL should support expressing mining functional requirements. For example, in Colombia, the safety regulation for underground mining works (decree 1886 of 2015) determines limits for the concentration of explosive and toxic gases. If any of these limits is exceeded, a series of actions/adaptations must be performed such as turning on alarms, activating the ventilation system, etc.

To better cope with these scenarios, our extended DSL offers new modeling primitives (see Fig. 8). All concepts that inherit from Region represent physical spaces. A Tunnel can be Internal or Access. Each mine access tunnel (Drift, Slope, or Shaft) must have one or more entrances (represented by the entries relationship). Finally, checkpoints (areas of the mine where gases, temperature, oxygen, and airflow are monitored) are specified through the CheckPoint concept. Each CheckPoint could contain multiple IoTDevices (sensors or actuators) represented by the devices relationship in the metamodel.

Fig. 8

Excerpt of the DSL extension metamodel

We offer a tree-based notation for modeling the relevant regions¹ that make up the mine structure. Figure 9 presents an example of the modeling of an underground mine containing two entries (Entry A and Entry B) in each of its inclined access tunnels (Slope A and Slope B), an internal tunnel (Internal), and a (Room) with two exploitation work fronts (W-front 1 and W-front 2).

Fig. 9

Mine structure modeling

At each mine control point, the airflow should be controlled by the fans. While very fast air currents can produce fires, very low air currents may not be efficient in dissipating gas concentrations. To involve control points directly in the adaptation rules, we have also modified our language. This extension of the DSL enables the modeling of conditions such as $(ControlPointA \rightarrow airFlow) > (2 m/s)$.

All the other concepts directly reuse the notation of the core DSL presented in Sect. 3.

5 Tool support

In this section, we describe the implementation and tool support for both our DSL and the core runtime framework responsible for monitoring and self-adapting IoT systems at runtime. Once both are described, we discuss the link between the two, i.e. the code generator that takes as input the model specified with our DSL and generates the code for runtime deployment and execution of the system.

5.1 DSL tool support

Our DSL is created using MPS, an open-source language workbench developed by JetBrains. By building the DSL on top of MPS, we automatically get a projectional editor for the DSL with facilities the implementation of the different notations highlighted in the previous sections. The DSL editor is freely available in our repository [20].

In particular, to develop the modeling environment for the DSL, we had to define in MPS three core elements: structure, editors and constraints. The structure is equivalent to the abstract syntax of the DSL. Projection editors define the desired Abstract Syntax Tree (AST) code editing rules. For our DSL We have defined textual, tabular, and tree view editors by implementing the mbeddr² extension of MPS.

For example, Fig. 10 shows the definition of the tabular editor for modeling the Sensor concept. We have used the partial table command to define the table structure (cells, content, and column headers). By defining this editor, the user is enabled to model Sensors using a tabular notation as shown in Fig. 4.

Fig. 10

Definition of the tabular editor for the Sensor concept

Finally, constraints restrict the relationships between nodes as well as the allowed values for properties. We have used this constraint mechanism to embed in the editor several well-formedness rules required in our DSL specification. For instance, we have added constraints to avoid repeated names, constraints to limit the potential values of some numerical attributes, constraints to restrict the potential relationships between nodes, and other constraints that prevent ill-formed models from being built.

5.2 Runtime tool support

Figure 11 summarizes an operational view of our architecture by distinguishing design time (left-hand side) and runtime (right-hand side).

At design time, the user creates an initial IoT system specification model using the modeling editor for the DSL described in the previous section. The code generator, presented in Sect. 5.3, transforms such a specification into a set of deployment and configuration options that describe a MAPE-K loop [9] which is performed at runtime. This section covers these components so that we can then describe in the next section how they are generated from the DSL definition.

The MAPE-K loop is a reference model to implement adaptation mechanisms in auto-adaptive systems. This model includes four activities (monitor, analyze, plan, and execute) in an iterative feedback cycle that operate on a knowledge base (see Fig. 11). These four activities produce and exchange knowledge and information to apply adaptations due to changes in the IoT system.

Based on the MAPE-K loop, our runtime architecture is composed of a set of components and technologies to monitor, analyze, plan, and execute adaptations as illustrated in the right-hand side of Fig. 11). We next describe how our architecture particularizes the generic MAPE-K concepts for self-adaptive IoT systems.

Fig. 11

Overview

5.2.1 Monitor

In the monitoring stage, information about the current state of the IoT system is collected and stored. The collected information is classified into two groups: (1) infrastructure and QoS metrics (presented in Table 1); and (2) information that is published in the system’s MQTT broker topics such as temperature, humidity, gas levels, and other types of sensor data. These two kinds of information are aligned with the addressed types of events to be detected, i.e., QoS events and sensor events.

We have implemented Prometheus Storage³ (a time-series database) to store the information collected by the exporters and monitors (such as kube-state-metrics⁴ and node-exporter⁵). Exporters are deployed to convert existing metrics from third-party apps to Prometheus metrics format. The information collected and stored can be queried in real time through the Prometheus user interface or the Grafana dashboard.

Table 1 QoS and Infrastructure metrics

5.2.2 Analyze

The information collected in the monitoring phase must be analyzed, and changes in the system that require adaptations must be identified. To deal with this, we have implemented Prometheus Alerting Rules to define alert conditions based on Prometheus query language expressions (PromQL) and to send notifications about firing alerts to the next MAPE-K loop phase. Each IoT system rule specified through the DSL is transformed into an alert rule of Prometheus.

5.2.3 Plan

According to the analysis made in the previous stage, an adaptation plan is generated with the appropriate actions to adapt the system at runtime. The adaptation plan contains the list of actions (scaling, offloading, redeployment, and operate actuator) that the user has defined for each rule via the DSL. In this stage, Prometheus Alert Manager is used to handle the alerts from the previous stage (Analyze) and routing the adaptation plan to the next stage (Execute). The adaptation plan is sent as an HTTP POST request in JSON format to the configured endpoint (i.e., to the Adaptation Engine).

5.2.4 Execute

In the Execute stage, the actions are applied to the IoT system following the actions defined in the adaptation plan. To achieve this, we have built an Adaptation Engine, an application developed using Python, flask, and the python API to manage the Kubernetes orchestrator. The adaptation engine can apply two sets of actions: (1) architecture adaptations through the orchestrator (e.g., autoscaling an application or offloading a pod); and (2) regular system operations such as controling a system actuator by sending it an instruction (e.g., a message to turn on or off an alarm). This adaption is generic and can be used to run any IoT system modeled with our DSL, including its mining extension.

5.2.5 Putting it all together: example scenario

To better understand how the different elements cooperate, we will exemplify how the framework monitors and executes tge rule specified in Fig. 7b. Code for the rule management is automatically generated ( Sect. 5.3), including YAML⁶ manifests for deployment, configuration and execution of the monitors, exporters, Prometheus, the Adaptation Engine and other software components implemented in the MAPE-K loop.

In the Monitoring stage, the exporters gather information about CPU consumption of the fog-f1 node. This information is stored in the Prometheus database. Then, in the Analysis stage, the condition of the rule is verified by executing query expressions in PromQL language. For example, the expression (executed by Prometheus Alerting Rules) that checks if the CPU consumption of the fog-f1 node exceeds 80% for 1 minute is presented in listing. 1 Note that we are calculating the average amount of CPU time used excluding the idle time of the node. If the condition is true, the alert signal is sent to the Alert Manager component of the next stage of the cycle (Plan). When the alert is received, the adaptation plan is built containing the two actions (offloading and scaling) and their corresponding information such as container to be offloaded, application to be scaled, number of instances, target nodes and target regions. In the Execute stage, the Adaptation Engine component first performs the Offloading action, and only if it fails, then the second action (Scaling) is performed.

5.3 Code generator

To configure and run the runtime infrastructure of an IoT system from its DSL model, we have implemented a model-to-text transformation that generates YAML files to deploy the IoT system’s container-based applications and the components of each stage of our MAPE-K loop based framework, including its internal logic. In particular, the generated code includes the following components:

• The container-based IoT applications specified in the input model. Following the running example of Sect. 2, the YAML manifests for deployment of containers C1, C2, C3, C4, and C5 are generated

• YAML Manifests to deploy the monitoring tools and exporters such as kube-state-metrics, node-exporter, and mqtt-exporter

• YAML code to deploy and configure the Prometheus Storage, Prometheus Alerting Rules, and Prometheus Alert Manager components. The PromQL code to define the rules (e.g., the code shown in Listing 1) is also generated as a Prometheus configuration file

• YAML manifiest to deploy the Adaptation Engine

• and the Grafana application to display the monitored data stored in the Prometheus database.

Due to space limitations, we do not show here the model-to-text transformation. You can find all this information in the project repository [20], including the generated code for the running example from Sect. 2.

6 Empirical evaluation

We have designed and conducted three experiments to validate our DSL and its accompanying infrastructure.

6.1 Language validation

We conducted two experiments to validate the expressiveness and ease of use of our DSL: Experiment 1, focused on specific mining concepts, and Experiment 2, focused on core architectural concepts, both based on the basic methodology for conducting usability studies [21]. Both also cover the modeling of adaptation rules.

We report here the results of Experiment 1. Full details of Experiment 2 are available on the project repository$^{7}$).

6.1.1 Experiment design and setup

We designed the experiment to validate the expressiveness and usability of the DSL regarding the modeling of the mine structure, the control points, sensors and actuators, and rules to manipulate such actuators (e.g., turn on the mine ventilation system when the methane gas sensor exceeds the threshold value).

Eight subjects participated in the experiment. Participants were experts from the mining domain, but had not been exposed to our DSL before. The goal was to check whether they were able to use it and get their feedback on the experience.

The experiment consisted of an asynchronous screening test (pre-questionnaire) to assess subjects’ prior knowledge and suitability for participation, and a synchronous exercise (virtual meeting) with two parts (Sessions 1 and 2). The materials and exercises provided to the participants, the questionnaires and the anonymized answers can be found in the repository of our DSL extended to the mining industry domain.⁷

• Pre-questionnaire (10 min): this screening questionnaire (Q0) was conducted prior to the start of Sessions 1 and 2 to ensure that participants had a basic level of mining knowledge including the structure of underground coal mines, gas monitoring systems, and modeling tools in this domain.

• Session 1 (50 min): In the first 20 minutes, we introduced basic knowledge of IoT systems and the use of the DSL implemented in MPS to model the structure of underground mines, the control points and the IoT devices deployed (sensors and actuators). Next, the participants performed the first modeling exercise about an underground coal mine (with the structure shown in Fig.  9), two control points (one at each working face) with three gas sensors and an alarm, a fan, and a control door in the internal tunnel. Each participant was provided with a virtual machine configured with the necessary software to perform the modeling exercise. Finally, the participants filled out a questionnaire (Q1) about the usability and expressiveness of the DSL to model the concepts of the first exercise.

• Session 2 (40 min): In Session 2, we first introduced the basic concepts of self-adaptive systems and the design of rules using our DSL. Next, participants performed the second exercise: modeling three rules involving sensor data and actuator operation. For example, if any of the methane gas sensors throughout the mine exceed the threshold value for 5 seconds, then turn on the fan and activate the alarms. Finally, participants completed the questionnaire Q2 to report their experience modeling the rules. Q2 also contained open-ended questions to obtain feedback on the use of the entire tool and suggestions for improvement.

The experiment was conducted in Spanish on three different dates in 2022. The first author of the paper conducted the virtual meetings and ensured that all were equally well executed.

6.1.2 Results

Four of the participants were involved in education (either students, teachers, or researchers), while the remaining four were involved in industry. All of them are aware of the terminology used in the design and structure of underground coal mines. Only two participants were not familiar with cyber-physical or IoT systems for mining. The modeling tools in mining context that they have used are AutoCAD⁸ and Minesight⁹ for the graphical design of the mine structure, and VentSim¹⁰ for ventilation system simulations. None of them were familiar with MPS.

Figure 12a presents the responses from questionnaires Q1 and Q2 related to the ease of use of the DSL. Most of them reported that the modeling of the mine structure, the control points, the devices (sensors and actuators), and the rules were easy. The results are positive and can also be evidenced by the number of right and wrong concepts modeled by the participants (Fig. 12b). The number of errors were low (12 of 188 modeled concepts): three incorrect Rule-conditions by wrong selection of the unit of measure, four incorrect Actuators by wrong assignment of actuator type and location within the mine, three missing Sensors not modeled, and two incorrect Regions (working faces) whose type was not selected.

Fig. 12

Validation results

Through the open-ended questions in questionnaires Q1 and Q2, participants suggested the following improvements to the DSL.

• Include the specification of the coordinates for each region and control points of the mine. Additionally, it would be useful to specify the connection between internal tunnels.

• The condition of a rule has a single time period. However, it would be useful to associate two time periods for conditions composed of two expressions. For example, the condition $tempSensorA > 30 C (10 seconds)$ && $tempSensorB > 35 C (20 seconds)$.

• The mine ventilation system can be activated periodically at the same time each day. It would be useful if the DSL could model rules whose condition is associated with the time of day.

6.1.3 Threats to validity

Although validation problems in empirical experiments are always possible, we have looked for methods to ensure the quality of the results, analyzing two types of threats: internal and external.

Internal validation concerns factors that could affect the results of the evaluation. To avoid defects in the planning of the experiments and the questionnaires (protocol), all authors of the paper discussed the protocol including the modeling exercises, the dependent variables, and the questions of the questionnaires. In addition, a senior researcher (not in the authors list) in empirical experiments validated the questionnaires. Another common thread is related to the low number of samples to successfully reveal a pattern in the results. Thirteen users total participated in this empirical validation. Eight participants were involved in Experiment 1, and five different ones in Experiment 2. Although the size of the participant group for this type of validation continues to be a matter of discussion, studies suggest 3 to 10 participants (depending on the level of complexity) are sufficient. For example, a popular guideline in this area is given by Nielsen and Landauer [22], who suggest that five participants are likely to discover 80% of the problems.

External validity addresses threats related to the ability to generalize results to other environments. For example, to validate the population and avoid sampling bias, we conducted a pre-questionnaire to the participants to ensure that they had the necessary basic knowledge and that there was no substantial difference between participants. In addition, at the beginning of each session, we introduced the definition of the concepts required for the experiment. It is important to emphasize that the participants of Experiment 1 were related to the topics of the mining domain, while those of Experiment 2 were computer science researchers.

6.2 Runtime framework validation

To evaluate the self-adaptation capability of our approach and the correctness of the code-generation and runtime infrastructure, we conducted experiments to test the architectural adaptations (scaling, offloading, and redeployment).

For each adaptation assessment we have designed a simple scenario in which an IoT system faces an event that forces adaptations. We have validated that starting from the IoT model including a specified rule triggered by such event, the code-generator creates the deployment infrastructure to run the IoT system and assess that such system actually adapts as expected when the event is triggered. We have collected and analyzed metrics such as CPU consumption, node availability, and time spent in each stage of the MAPE-K loop to also show the benefits of such self-adaptive architecture when the system is under pressure.

The design of these experiments and the results are in the appendices of the paper that can be found in the project repository.¹¹

7 Related work

Modeling of cloud architectures has been widely studied, including provisioning of resources for cloud applications. Nevertheless, most proposals do not cover multi-layered architectures involving fog and edge nodes. This is also the case for Infrastructure-as-code (IaC) tools.

Some works such as [23,24,25] focus on the cloud layer. Others, like [5,6,7] are oriented towards the edge and fog layers. In particular, studies such as [5, 6] focus on the modeling of sensors, actuators, and software functionalities, while [7] addresses the modeling of application deployment at the fog layer. Works like [26, 27] are oriented towards lower-level communication aspects and individual IoT component behaviours.

A few proposals are closer to ours in terms of the static modeling of IoT systems, such as [28, 29] but using less expressive DSLs and, especially, limited possibilities when it comes to the definition of rules, including self-adaptive ones.

Indeed, support for (self-adaptive) rules is less covered by previous approaches. For instance, rules for cloud architectures are the topic of works such as [30,31,32,33] that propose only partial solutions, as they either restrict the parts of the system that could be adapted or offer a limited expressiveness in the definition of the rules (e.g., no trigger condition).

Among the most powerful solutions, Lee et al. [4] present a self-adaptive framework where IoT systems are modeled as finite-state machine. However adaptations are only enabled at the device layer level and self-adaptive rules are not possible. Garlan et al. [34] present Rainbow, a framework for adapting a software system when a constraint (expressed in terms of performance, cost, or reliability) is violated. Similarly, Weyns et al. [35] propose MARTA, an architecture-based adaptation approach to automate the management of IoT systems employing runtime models. However, neither Rainbow or MARTA cover the specification of multi-layer architectures, nor rules combining them, and their rules are coarse-grained (e.g. not at the container level). Finally, Petrovic et al. [29] propose SMADA-Fog, a semantic model-driven approach to deployment and adaptation of container-based applications in Fog computing scenarios. SMADA-Fog does not allow the specification of complex adaptation rules composed of various conditions and actions. Moreover, grouping nodes and IoT devices according to their location is not possible, forbidding the possibility to apply adaptations on group of nodes belonging to a cluster or a given region.

Concerning the specification of IoT systems in underground mining domain, works such as [36,37,38] focus on the design or deployment of the device layer (sensors and actuators) of monitoring systems, but do not address the mine structure specification, runtime adaptations, and deployment of containerized applications. To sum up, ours is the first proposal that enables the specification, deployment and execution of multi-layer IoT architectures (device, edge, fog, and cloud) and the definition of complex rules covering all layers (and combinations of) involving multiple conditions and actions that can, potentially, engage groups of nodes in the same region or cluster of the IoT system. Moreover, our proposal can be easily extended and specialized in different domains as shown with the underground mining scenario.

8 Conclusions and future work

We have presented a model-based approach for the specification and runtime execution of multi-layered architectures of IoT systems and their self-adaptation rules. Our approach comprises a new DSL to model such systems, a code generator, and a runtime infrastructure, based on the MAPE-K loop, to monitor and execute the IoT system at runtime based on a variety of rules, involving architectural adaptations and rules to address functional requirements. The full process is assisted by a set of open-source tools that have been released as part of this work. We have also validated the usability and extensibility of the DSL.

As part of our future work, we will continue to enrich the DSL based on the suggestions made by the experiment participants, including a new visual renderer of the modeled architecture to complement the current projections. We also plan to facilitate the definition of complex self-adaptive rules by predefining a set of common patterns such as canary, rolling update, and blue-green deployment strategies that could be directly referenced in the definition of a rule. Moreover, we are also interested to automatically discover potentially useful adaptation rules by analyzing historical log data from the IoT system (e.g., focusing on previous system crashes) with machine learning. Finally, we are creating additional extensions to the DSL. In particular, one to model Wastewater Treatment Plants as part of an ongoing European project.