Computing at the Edge: Challenges and Priorities for Software Engineering and AI
Unlike traditional computing, where processing is mainly performed on local servers and in the cloud, edge computing pushes applications, data, and computing power to the edge of the Internet—to mobile devices, sensors, and end users. The number of devices connected to the Internet, and the volume of data being produced by those devices and used by governments and businesses, is growing far too quickly for traditional computing approaches and data center infrastructures to accommodate. In addition, artificial intelligence and machine learning (AI/ML) have the potential to add autonomy to these systems, allowing them to operate in areas where operators are unable to go.
Moving compute to the edge has many benefits but also presents significant challenges for software and AI engineering. This brochure describes eight such challenges. The SEI has a dedicated applied research and development team who creates and transitions innovative solutions, principles, and best practices for architecting and developing systems to support teams operating at the tactical edge and using AI/ML techniques for improved capabilities and mission support. Contact us to learn more about the benefits of edge computing and how we can collaborate to solve today's and tomorrow's edge computing challenges.