Edge computing is an exciting new approach to network architecture that leaves behind cloud computing and places the intelligence for processing data closer to the sources. Inherently, this offers many technical advantages, such as reduced latency, secure decentralized processing and storage, scalability at lower complexity, versatility to adapt the nodes to the underlying application to be serviced, and increased reliability.
Edge computing can dramatically boost services and applications by supporting artificial intelligence (AI) natively, instead of relying on AI in the cloud. Edge computing supporting AI (without cloud intervention) is the only technology that will enable many of the long-awaited game changers: Factory 4.0 and smart manufacturing, 5G, Internet-of-things, self-driving vehicles, remote robotics for healthcare, machine vision, among others.
Introducing AI to edge computing is not just a software process; specific hardware needs to be designed with big data processing and AI in mind. The BRAINE project’s overall aim is to boost the development of the Edge framework and, specifically, energy efficient hardware and AI empowered software systems, capable of processing Big Data at the Edge, supporting security, data privacy and sovereignty. BRAINE’s overall aim will be reached by targeting five fine-grained goals:
Devising an EC infrastructure that offers control, computing, acceleration, storage, and 5G networking at the Edge and excels in scalability, agility, security, data privacy, and data sovereignty in Big Data and AI for low latency and mission-critical applications.
Developing a future-proof Edge security framework and associated infrastructure. based on the latest software and hardware security technologies.
Developing a distributed and partly-autonomous system that takes data privacy and sovereignty into account on each and every decision regarding workload placement, data transfer, and computation, while guaranteeing interoperability with the environment.
Developing a heterogeneous, energy efficient Edge MicroDataCenter, suitable for stationed, mobile, and embedded autonomous applications, that goes beyond the current hardware and software architectures and offers Big Data processing and AI capabilities at the Edge.
Testing and demonstrating the effectiveness and generality of the BRAINE approach by evaluating multiple real-world use cases and scenarios that exhibit the required scalability, security, efficiency, agility, and flexibility concerns.
The BRAINE approach is based on creating a seamless Edge by building clusters of nodes, forming an Edge MicroDataCenter (EMDC), along with smart network interface cards (NIC) capable of pairing with Edge devices to enable high speed connectivity and native AI. The impact of BRAINE encompasses advances in the European video distribution ecosystem, by developing new edge computing architectures and subsystems, improving data processing at the network edge, by supporting scale-up (elastic resource allocation) and scale-out approaches (dynamic clustering of proximate virtualized Edge resources). This will lead to unprecedented savings in performance and energy efficiency: 2x performance/ Watt and 50% energy savings on control and computing functions; 71% latency reduction for an acceleration centric EMDC; 80% space and maintenance reduction, including a 99.999% fault tolerance with level 5 autonomy (autonomous driving, robotics, mission critical system); and significantly faster infrastructure installation and deployment. BRAINE will also impact network-edge security and data privacy, especially when high-throughput data are considered, by combining security/privacy communication standards/protocols with security/privacy technologies currently utilised in smart grid, such as Transport Layer Security (TLS).
BRAINE provides a new vision for utilizing edge resources by providing novel network-edge workload distribution schemes. Predicting resource availability and workload demand, identifying trends, and taking proactive actions are all aspects of the novel workload distribution. The workload distribution technology developed in the context of BRAINE can be transferred to many other edge/fog computing environments to achieve different goals. Last but not least, BRAINE will have an important positive impact on the environment. Through BRAINE, edge computing can reduce this projected energy consumption by offloading many of the AI functions next to the end-users.
BRAINE will demonstrate edge computing enabling AI through four use cases: healthcare assisted living (case 1), hyperconnected smart city (case 2), robotics in Factory 4.0 (case 3) and supply chain Industry 4.0 (case 4); the use cases are supported by organizations with specific domain expertise.
Through the achievement of its goals, the BRAINE project will help to position Europe at the forefront of the intelligent edge computing field, enabling growth across many sectors (manufacturing, smart healthcare, surveillance, satellite navigation, and others). By lowering the barriers for utilising edge computing for artificial intelligence applications, BRAINE will open the door for European SMEs to leverage state of the art technologies, driving their development and growth as industry leaders in their sectors.
