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Edge Computing - Next Steps in Architecture, Design and Testing

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Edge Computing Next Steps in Architecture, Design and Testing Edge Computing: Next Steps in Architecture, Design and Testing Introduction While edge computing has rapidly gained popularity over the past few years, there are still countless debates about the definition of related terms and the right business models, architectures and technologies required to satisfy the seemingly endless number of emerging use cases of this novel way of deploying applications over distributed networks. In our previous white paper the OSF Edge Computing Group defined cloud edge computing as resources and functionality delivered to the end users by extending the capabilities of traditional data centers out to the edge, either by connecting each individual edge node directly back to a central cloud or several regional data centers, or in some cases connected to each other in a mesh. From a bird's eye view, most of those edge solutions look loosely like interconnected spider webs of varying sizes and complexity. In these types of infrastructures, there is no one well defined edge; most of these environments grow organically, with the possibility of different organizations owning the various components. For example, a public cloud provider might supply some of the core infrastructure, while other vendors are supplying the hardware, and yet a third set of integrators are building the software components. Trying to create a one size fits all solution is impossible for edge use cases due to the very different application needs in various industry segments. Interestingly, while cloud transformation started later in the telecom industry, operators have been pioneers in the evolution of cloud computing out to the edge. As owners of the network, telecom infrastructure is a key underlying element in edge architectures. After four years, while there is no question that there is continuing interest in edge computing, there is little consensus on a standard edge definition, solution or architecture. That doesn't mean that edge is dead. Edge must be by its very nature highly adaptable. Adaptability is crucial to evolve existing software components to fit into new environments or give them elevated functionality. Edge computing is a technology evolution that is not restricted to any particular industry. As edge evolves, more industries find it relevant, which only brings fresh requirements or gives existing ones different contexts, attracting new parties to solve these challenges. Now more than ever, edge computing has the promise for a very bright future indeed! This document highlights the OSF Edge Computing Group's work to more precisely define and test the validity of various edge reference architectures. To help with understanding the challenges, there are use cases from a variety of industry segments, demonstrating how the new paradigms for deploying and distributing cloud resources can use reference architecture models that satisfy these requirements. Challenges in different industries In a nutshell, edge computing moves more computational power and resources closer to end users by increasing the number of endpoints and locating them nearer to the consumers -- be they users or devices. Fundamentally, edge computing architectures are built on existing technologies and established paradigms for distributed systems, which means that there are many well understood components available to create the most effective architectures to build and deliver edge use cases. This section will guide you through some use cases to demonstrate how edge computing applies to different industries and highlight the benefits it delivers. We will also explore some of the differentiating requirements and ways to architect the systems so they do not require a radically new infrastructure just to comply with the requirements. 5G Brings You the Edge or Vice Versa? 5G telecom networks promise extreme mobile bandwidth, but to deliver, they require massive new and improved capabilities from the backbone infrastructures to manage the complexities, including critical traffic prioritization. The network needs to provide both high throughput and low latency combined with efficient use of the available capacity in order to support the performance demands of the emerging 5G offerings. Signaling functions like the IMS control plane or Packet Core now rely on cloud architectures in large centralized data centers to increase flexibility and use hardware resources more efficiently. However, to get the same benefits for user plane and radio applications without bumping into the physical limitations of the speed of light, compute power needs to move further out to the edges of the network. This enables it to provide the extreme high bandwidth required between the radio equipment and the applications or to fulfill demands for low latency. The most common approach is to choose a layered architecture with different levels from central to regional to aggregated edge, or further out to access edge layers. The exact number of levels will depend on the size of the operator network. The central locations are typically well equipped to handle high volumes of centralized signaling and are optimized for workloads which control the network itself. For more information about signaling workloads, reference Chapter 2.1 of the CNTT Reference Model under Control Plane for a list of examples. To increase end-to-end efficiency, it is important to pay attention to the separation of the signal processing and the user content transfer. The closer the end users are to the data and signal processing systems, the more optimized the workflow will be for handling low latency and high bandwidth traffic.

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