Edge computing is a game changer for the IoT. It allows IoT devices to be more independent, storing, processing, and analyzing data locally instead of just sending it to a centralized server. This can improve the effectiveness of existing IoT devices, and make new devices and deployment topologies possible.
The Internet of Things (IoT) refers to the process of connecting physical things to the Internet. The IoT consists of physical devices or hardware systems that receive and transmit data over a network without human intervention. A few examples are sensors, autonomous vehicles, smart homes, smart watches, and industrial IoT devices.
A typical IoT system works by continuously sending, receiving, and analyzing data in a feedback loop. Analytics can be performed in near real-time or over long periods of time, and is often aided by artificial intelligence and machine learning (AI/ML) algorithms to help derive insights from massive data volumes.
Edge computing involves moving computing, storage, and networking functions at or near to the physical location of users or data sources. By moving computing services closer to these locations, users benefit from faster, more reliable services and better user experience, and organizations have the ability to deploy new types of latency-sensitive applications.
Edge computing, when combined with the IoT, makes it possible for organizations to flexibly deploy workloads on IoT hardware, improving performance and enabling new use cases, including low latency and high throughput data, which were not possible with the traditional IoT.
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Internet of Things applications often work as monitoring systems that collect and analyze data to trigger informed actions. IoT apps might process data daily, hourly, or in respond to external triggers. Edge computing benefits IoT by moving computing processes closer to the device, reducing network traffic and latency to enable real-time insights.
IoT devices often send small data packets back to a central management platform for analysis. This system works well for some applications, but the expected growth of IoT means that future networks will be overburdened with devices. Edge computing optimizes bandwidth and only sends long-term storage data to the central platform, not all data.
Managing security is another major challenge for organizations with large numbers of IoT devices. Attackers could exploit the large volume of connected devices to execute DDoS attacks. Edge computing does not automatically provide more security than private clouds, but the localized approach makes it easier to manage security. For example, it is useful for data sovereignty and compliance with local data protection regulations.
Here are three common options for edge computing architecture:
Related content: Read our guide to edge computing architecture (coming soon)
IoT edge computing systems have made tremendous progress over the past few years. Here are the most common features of edge computing and how they have evolved.
Machine learning (ML) plays a key role in IoT edge runtimes and IoT applications, and many DevOps teams are incorporating machine learning into their application designs. Machine learning allows organizations to analyze and make predictions based on the data stored and processed by IoT devices.
ML application programming interfaces (APIs) can analyze data from IoT devices to identify data patterns, user behavior, trends, and more. By carrying out this analysis at the edge, an organization can reduce the time required for processing, and can continuously update the analysis based on real time data from IoT devices.
IoT gateways support device-to-device and device-to-cloud communication. Their key features include data filtering and analysis. They can also be programmed to handle authentication of data that needs to be sent to a cloud service. This can improve security for IoT data transfers.
When an edge agent needs to communicate with another device or the cloud, the IoT gateway processes the request, clears it, and sends the information to the destination. Organizations can analyze the transmitted data and use the results to monitor how the IoT network operates and improve system efficiency.
Kubernetes, the platform on which the Run:AI scheduler is based, has a lightweight version called K3s, designed for resource-constrained computing environments like Edge AI. Run:AI automates and optimizes resource management and workload orchestration for machine learning infrastructure. With Run:AI, you can run more workloads on your resource-constrained servers.
Here are some of the capabilities you gain when using Run:AI:
Run:AI simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their deep learning models.
Learn more about the Run.ai GPU virtualization platform.