The State of the Art in IoT Orchestration: From Rules Engines to Orchestration
Connected and smart devices are proliferating quickly, increasing efficiency and safety in industrial, home, and infrastructure use cases. You might think that they all plug into some common interface and behave in sync with each other… but they don’t. Several products have emerged to try and bridge the gaps and enable coordination, and they each take different approaches to the goal of orchestration.
Sometimes, your Internet of Things (IoT) devices just need to perform isolated tasks, like “set off an alarm when the temperature rises.” Sometimes, a family of devices are able to coordinate within a shared platform, like smart thermostats or appliances. For mission-critical use cases, though, we’re starting to expect devices to coordinate with one another to achieve more complex goals and deal with a greater range of circumstances.
This is where IoT orchestration comes in—a crucial process that ensures IoT devices collaborate seamlessly, efficiently, and securely. This sounds a lot like orchestration in the cloud context, a topic we’ve discussed in prior blog posts. But in the context of IoT, orchestration is a particularly complex and almost impossible task.
Orchestration in a Data Center: Complex, but Doable
In the cloud, which operates within vast data centers, resources are allocated dynamically through container orchestration. Tools like Kubernetes or Docker Swarm orchestrate cloud resources by knowing where all processes are running, assessing available resources, and shifting workloads to ensure that processes always have what they need.
While complex, cloud orchestration is well-established and indispensable. It works because data centers offer the consistent internal connectivity, abundant computational power, and ample storage required to run these orchestration tools effectively.
Orchestration in the Wild: The Challenge for IoT
However, IoT environments present unique challenges that make cloud-style orchestration impractical. IoT devices often operate in remote or resource-constrained settings where they lack the stable connections and computational resources that are taken for granted in data centers.
Some of these challenges include:
Resource Constraints: Many IoT devices, especially those deployed at the edge, have limited CPU, memory, and power, making it difficult to implement complex orchestration logic.
Intermittent Connectivity: IoT devices often experience unreliable communication due to network variability, which makes real-time coordination challenging.
Diverse Device Ecosystems: IoT environments frequently consist of a wide variety of devices, protocols, and manufacturers, requiring highly interoperable platforms to facilitate effective orchestration.
Latency and Real-Time Requirements: Many IoT applications, such as drone swarms or industrial automation, need immediate decision-making at the edge, where delays could lead to failures.
Because of these constraints, the type of orchestration that works in data centers does not translate well to IoT systems. Instead, several approaches have evolved to tackle IoT orchestration challenges.
Rules Engines: Automating with Predefined Logic
The simplest and most established approach to IoT orchestration is the use of rules engines. These engines automate device behavior based on predefined conditions—"if this, then that." For example, if the temperature reaches a certain threshold, a rules engine can trigger an alert or switch on an air conditioning unit.
While effective for basic automation, rules engines fall short in dynamic environments where devices must respond to unforeseen events or work together to achieve complex goals. Rules engines cannot adapt in real-time to new information, making them inadequate for mission-critical applications where flexibility and real-time decision-making are crucial. Furthermore, they don’t provide the broader context needed to understand why certain actions were taken across a distributed IoT system.
AI-Enabled Orchestration: Adaptation and Intelligence
As IoT environments become more complex, AI-enabled orchestration offers a more dynamic approach. AI models can be used to analyze patterns, predict future events, and adjust the behavior of IoT devices in real-time. For example, AI can analyze traffic patterns in a smart city to adjust traffic lights dynamically or reroute vehicles to reduce congestion. In industrial applications, AI models can optimize the operation of factory equipment based on real-time data from sensors.
AI-enabled orchestration introduces flexibility and adaptability, allowing IoT systems to react to a greater range of circumstances. However, this approach comes with challenges of its own:
Garbage in, Garbage Out: Without a solid grounding in the real-time state awareness of the system, AI orchestration lacks the context needed to ensure that its actions are aligned with the conditions of the devices and systems it's orchestrating.
Complexity of Integration: AI models often require complex integrations across different layers of the technology stack, which can increase the deployment burden on organizations.
Black Box Nature: AI decisions can sometimes be difficult to explain, which creates risks in critical systems where traceability and accountability are important. While AI can enhance orchestration, its unpredictability and complexity can be problematic for highly regulated industries like healthcare or finance.
Resource Demands: AI orchestration often requires significant computational resources, which many IoT devices—particularly those at the edge—do not possess.
AI-enabled orchestration is simply not a standalone solution, because it can only build on a shared, common operating picture; it cannot create that state awareness itself. The same problem for Orchestration in IoT exists for AI-enabled Orchestration in IoT.
Finally: Consensus-Based Orchestration for IoT
Consensus-based state management, similar to the foundation of Kubernetes, Kafka, and every other cloud based distributed system, would be the holy grail for IoT orchestration. That’s because consensus-based state management provides the common operating picture.
Until recently, consensus-based systems were too resource-intensive and difficult to design for widespread use in IoT. They tend to slow processes to a crawl whenever they are implemented outside of the controlled data center environment (for example in blockchain and some banking use cases.)
However, with the introduction of enterprise-grade state management platforms like Cachai’s Altiro, this approach is now feasible. Mission-critical IoT orchestration can now be more like data center orchestration.
In a consensus-based system, the state of the system is agreed across all of the devices. This ensures that all devices contribute to and act upon a common understanding of reality—even in environments with unreliable networks or device failures. This is critical in scenarios where devices must collaborate to achieve mission-critical goals, such as autonomous drones coordinating in a swarm, or sensors in a smart factory responding to equipment failures.
Benefits of Consensus-Based Orchestration:
True Smart Orchestration: Consensus-based orchestration enables you to transcend even sophisticated rules engines and build a system that understands its own context and purpose.
Fault Tolerance: In scenarios where devices lose connectivity, consensus ensures that devices can rejoin the group and continue functioning without causing system-wide failures.
Data Integrity: Consensus-based systems guarantee that updates and actions are only executed when the majority of devices consent, preventing any rogue or faulty device from corrupting the system.
Performance in Unreliable Networks: Unlike traditional cloud orchestration, consensus-based state management can handle environments where connectivity is intermittent, allowing devices to operate effectively even in challenging conditions.
Why Now? The Role of Enterprise-Grade State Management
The reason consensus-based orchestration hasn’t been widely adopted until now is simple: It requires a robust foundation of enterprise-grade state management to function properly. The introduction of state management platforms, like Altiro, finally enables IoT devices to synchronize state across distributed networks without relying on continuous cloud connectivity.
These platforms provide a secure, scalable way to manage consensus even in highly distributed environments, allowing IoT devices to maintain a shared understanding of their environment. By ensuring a persistent, auditable state, platforms like Altiro offer a level of reliability and real-time consistency that was previously unattainable.
Conclusion: The Future of IoT Orchestration
In summary, while rules engines offer basic automation, and AI-enabled orchestration brings adaptability, consensus-based state management provides the foundation necessary for mission-critical IoT orchestration. With enterprise-grade state management platforms now available, IoT systems can achieve new levels of reliability, security, and coordination—enabling connected devices to work together in ways that were previously unimaginable.
IoT orchestration is entering a new era, and with the right tools, the possibilities are limitless.