In computing, a workload refers to application resources used to support a common business goal. A workload can also describe the resources used to execute common processes. IoT devices, on the other hand, are used to monitor, connect and control physical assets.
IoT workloads are applications that interact with IOT devices, utilizing the available resources to receive and process data from physical devices like actuators and sensors. These workloads can be challenging to manage due to their characteristics and dynamism. Businesses that leverage these solutions must also consider how they can balance cost with performance.
Understanding IoT Workload Characteristics
Before looking at how engineers can leverage Kubernetes to optimize IoT workload resource utilization, we should understand what inherent characteristics these workloads possess that make this such a challenge.
IoT devices typically send and receive intermittent data. This leads to small periods of high bursts of traffic followed by periods of inactivity. Having a server or server cluster running all the time for this type of data would be incredibly wasteful, mainly due to the long periods of inactivity.
Second, IoT has very low latency requirements. The whole point of using IoT devices is to receive real-time data as quickly and efficiently as possible. For this reason, any solutions used to manage them and their workloads need to have the lowest latency possible.
Engineers must also consider that IoT devices have limited resources available to them. These include low storage capacities, memory, and processing power that can all impact the performance of their workloads.
Even with these characteristics presenting significant challenges that require creative solutions, IoT has emerged as the perfect solution for specific applications.
We know that many countries are pushing for the development of smart, interconnected cities of the future. IoT devices are perfect for this, with their workloads in such applications including environment sensing, traffic monitoring, smart lighting, and ensuring low-latency, high-availability connectivity between devices.
IoT devices are also used a lot in industrial settings. Here, their workloads include quality control, predictive maintenance, and machine monitoring.
One place many people will recognize the use and need of IoT workloads is in consumer applications. Here, IoT workloads are crucial in wearable devices, smart homes, and connected cars.
The Intersection of Kubernetes and IoT Workloads
Kubernetes is an open-source platform that makes managing containerized services and workflows much easier. It is also the perfect solution for automating container management, with its in-built commands helping with the deployment, iteration, and scaling of applications and services.
Businesses can set up Kubernetes clusters as they need them. However, it is best to rely on a managed Kubernetes provider to provision the resources you need and let their applications and services handle everything else. This includes handling the underlying infrastructure, updating Kubernetes, managing nodes, and managing the control panel.
The features that apply best when using Kubernetes for IoT resource management include:
- Scalability – Kubernetes is excellent at scaling IoT applications up and down depending on demand. This aligns well with the need for IoT workloads to send and receive intermittent data
- Portability – Businesses that leverage the technology can easily move their applications to different environments or even onto on-premises infrastructure
- Management – Kubernetes reduces operational overhead by simplifying the management of IoT applications. Businesses can lean further into this by working with a managed Kubernetes provider who handles everything for them.
Challenges That Arise When Optimizing IoT Resource Utilization
A common issue when managing resource utilization in IoT workloads is resource fragmentation and contention. If the underlying infrastructure is not robust enough or set up well, multiple workloads can end up competing for the same resource. This can lead to resource contention and fragmentation, both of which lead to degraded performance.
A second issue is inefficient resource allocation and scheduling. While Kubernetes has an excellent scheduling algorithm, it might not be the best solution for IoT workloads.
This can lead to inefficient resource allocation, where some workloads get more resources than they need while others get too few. Such inefficiencies can lead to further performance bottlenecks.
Lastly, there can be over-provisioning and under-provisioning of resources due to inefficient allocation. The former can lead to wasted resources, while the latter can lead to the performance bottlenecks mentioned above.
Strategies You Can Use When Optimizing Resource Utilization for IoT Workloads
Vertical scaling is a common solution to this challenge. It entails adjusting the resources you allocate to individual pods. These include memory, CPU, and storage allocated to different periods depending on what they require.
Also, adjust resource allocations between IoT workloads. Start by analyzing the memory, storage, and computational power each needs, considering their unique requirements.
For example, IoT workloads that require intense data processing might need higher CPU resources, while those that store vast amounts of data might need more storage allocated to them.
The second option is horizontal scaling. With this strategy, engineers remove or add pods depending on their workload demands. Kubernetes makes it easy to do this through its auto-scaling mechanisms that adjust the number of pods required by each workload based on its metrics.
Lastly, configure the cluster auto-scaling policies to ensure all the IoT applications you run or wish to run have the resources they need to meet demand.
Another strategy is using resource quotes and limits; resource limits are crucial for stopping resource exhaustion. Also, setting limits for the number of resources each pod can consume is crucial for ensuring proper resource allocation.
Engineers must also decide what resources are fair for each workload. When doing this, consider setting quotas and namespaces to ensure an equitable distribution of resources.
Lastly, embrace containerization best practices. Start by optimizing your container images for performance and use. You can do this by removing unnecessary dependencies and files. Additionally, only choose base images optimized for IoT workloads. Doing so will help you avoid unnecessary dependencies in the first place.
Conclusion
Optimizing resource utilization for IoT workloads is crucial due to their characteristics, demands, and requirements. Doing this helps avoid issues like resource starvation and exhaustion, scaling, inefficiencies, and performance bottlenecks.