Challenges Edge Computing – com! professional

What developers need to master in order to master the future challenges of edge computing, AI and cloud computing.

Matt Jones: Edge technology expert and vice president of global engineering at Wind River


This post was written by Matt Jones, Edge Technology Specialist and Vice President Global Engineering at Wind River
The next phase of digital transformation takes place at the intelligent network edge. Application areas such as autonomous vehicles, intelligent energy networks and advanced industrial robots require the processing of large amounts of data in real time. Only in this way can systems make intelligent decisions directly in the smart end devices at the edge of the network, instead of having to manage these processes via central data centers. The advancing introduction of 5G in Europe, North America and parts of Asia provides the basis for the fast response times required for edge computing. The trend towards cloud-native technologies, fueled by AI, 5G and intelligent systems at the network edge, is leading to an increase in the demand for developers with new skills. Edge technology expert Wind River highlights the central challenges for companies and developers of distributed cloud environments and summarizes particularly important skills and approaches.

Edge solutions and the increasing provision of cloud services in company networks are accelerating the need for distributed cloud infrastructures. In connection with the global transition to 5G, according to many experts, distributed cloud will emerge as the preferred infrastructure model, driven by the increasing demand for flexibility, low latency times and the support of a decentralized workforce. However, the deployment and development of intelligent edge systems brings with it a number of challenges from the old and new world.

Generation change in systems and developers

We are in a phase of upheaval. New technology trends, constantly changing customer requirements as well as new business models and dynamics in the markets create a precedent for what is expected of developers in the coming years. Systems should be as autonomous as possible and critical data must be processed in real time. In the meantime, legacy code continues to run in embedded systems for important sectors – such as industry, aerospace, defense, transportation, utilities – because it takes time to modernize these systems with more computing power, automated and intelligent functions. In addition, there is an age shift in the development workforce from baby boomers to millennials and Generation Z.

The generation of developers who are familiar with legacy code in older languages ​​is about to retire. At many universities, however, the training of the next generation in working with these legacy systems plays a subordinate role. For companies that want to bring more flexibility to their business processes and infrastructure and want to meet new requirements, this can be a major hurdle. So you need to act quickly and invest in appropriate training and tools to support this shift in your workforce and address skill gaps. While this may seem like uncharted territory, it is primarily an opportunity to align the future of intelligent edge systems with the modern cloud-native skills and mindsets of today’s college graduates.

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