IoT is helping businesses and industries to transform into data-driven systems, opening up new opportunities. Serverless computing (in the form of Function-as-a-Service) can efficiently execute IoT applications, but it’s best to use a cold start rather than a warm one. Here we explore the best way to employ serverless computing for IoT. Experts opine that cold start model works best when data transmission is slower.
Fast IoT adoption is supported by technology companies through the innovation which are affordable; newer manufacturing companies are emerging to provide low-cost and high-end devices as well as IoT platforms that allow for device integration and management. ESP32 is an example SoC which is presently used both by the hobbyists and by the smaller companies.
The pros and cons of serverless architecture
Serverless computing is a novel way to manage IoT traffic. You can use this software to monitor messages and events in your system. It is a serverless solution, meaning that you won’t need resources from external servers. This will be optimal for IoT (Internet of Things) applications.
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With the cloud, you can purchase only the features that you need, according to a pay-as-you-go model. You do not need to run and maintain a server instance just for using certain features.
You can get a low-cost solution with thousands of connected IoT devices. But with that many devices reporting data every second, you might be paying hundreds per month to operate them. So, the serverless architecture is usable in real life only in certain situations. The average number of messages to be exchanged for implementing the overall machinery depends on the bandwidth and deployment complexity.
Reducing the number of requests per month with 10% will lead to a 10% reduction in monthly costs.
What is about edge computing?
It’s predicted that serverless computing will have a substantial impact on IoT because it is able to perform tasks on both the cloud and the edge.
Edge computing is an alternative to centralized data centers. It allows for cost reduction and access to unlimited storage, but not all applications are compatible with the cloud. In particular cases, the stochasticity of the access to services in the cloud is unsuitable for supporting data from sensors and for providing a service quickly. Resource management technologies that optimize resources will be necessary.
What is the conclusion?
The cold start and warm start have different levels of resource consumption. The overall complexity in both computing and network resource changes the level as well.
The traditional implementation cost for architecture doesn’t depend on the number of devices or the request per second, but rather the trade off in operational expenses. Not all services are equal. If the service time requirement is loose, you can use a cold start implementation with success.
