Edge computing is aimed at enabling analytics on devices that are close to physical data sources. Edge devices are generally characterized by limited computational resources and near-real-time responses to the needs of users. Application scaling on edge devices is a key success factor for edge computing and requires a software architecture that regards cloud and edge as a continuum of resources whose use is determined dynamically at runtime.
Fei Li explores the challenges of application scaling in edge computing with an analytics application in the context of the industrial internet of things (IIoT). Edge devices that are directly connected to industrial equipment, such as sensors, robots, and meters, are utilized to provide near-real-time analytics. The cloud is leveraged on the fly to offload analytics jobs when resources on the devices do not suffice. Microservice architecture is adopted on both cloud and edge with a strong focus on critical requirements in the IIoT—safety, performance, and reliability—which cannot be compromised during application execution.
Fei Li is a software architect and research scientist at Siemens AG in Austria, where he designs and develops prototypes in the fields of edge computing, industrial automation, and manufacturing management, working closely with various production teams to transfer practical knowledge into production. Fei has more than 10 years of experience designing and developing large-scale distributed software. He has authored more than 30 publications on the subjects of service-oriented computing, the internet of things, and smart city solutions.
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