Microservices at the Edge - Best Practices

IoT Edge processing as an architectural component emerged to meet the demands of reducing network bandwidth requirements and lowering response latency. Microservices as a software paradigm evolved to …

IoT Edge processing as an architectural component emerged to meet the demands of reducing network bandwidth requirements and lowering response latency. Microservices as a software paradigm evolved to facilitate agile development of large-scale systems. IoT applications are sensor data triggered, matching well the event-driven paradigm of Microservices. However, Edge resources (CPU/RAM/storage/network capacity and availability) can range from marginal to significant depending on the use case. How does this affect Edge Microservice design? There are ramifications from number and payload size of inter-service calls, data caching, database accesses, network connectivity and capacity, system scalability, and consistency. We illustrate their effects in the context of EdgeX, an open source Edge IoT project, and share performance results from the Delhi, Edinburgh, and Fuji releases. We conclude with best practices, customizable deployments, and the need for monitoring and management.