42 research outputs found
5G service delivery platform: 360 VR vertical as an example
No full textOperators have been adopting the cloud-native paradigm for the roll-out of 5G architecture. With it goes the introduction of the service-based architecture as a key design pattern for realizing future control- and ultimately user-planes of mobile networks. The main benefits of this new design pattern are increased flexibility, meaning new business cases may be addressed while maintaining competitive cost levels, and also the ability to realise use cases which historically have required the full breadth of infrastructure level knowledge. We present a realization of a service delivery platform entirely built upon service-based architecture principles
Cloud-native 5G service delivery platform
No full textOperators have been adopting the cloud-native paradigm for the rollout of 5G architecture. With it goes the introduction of the service-based architecture as a key design pattern for realizing future control, and ultimately user planes of mobile networks. The main benefits of this new design pattern are the increased flexibility to address new business cases while maintaining competitive cost levels, while also benefitting the realization of use cases usually requiring the full breadth of infrastructure level network slicing. In this paper, we present a realization of a service delivery platform entirely built upon service-based architecture principles, showcasing the realization of control as well as user plane services along with early deployment insights
D3.10: FLAME platform architecture and infrastructure specification v2: A cloud-native service delivery platform
No full textThis report provides the second version of the FLAME platform architecture and infrastructure specification. It outlines therefore the blueprint that forms the basis for further specification but more importantly the implementation work towards a deployable FLAME platform within the infrastructures of Bristol and Barcelona, and other sites. The report builds on the first version of the architecture and focuses on the key updates to that first version
Operating a next generation media urban testbed
No full textThe adoption of 5G promises low latency and high throughput, greater adaptability of highly distributed compute and storage infrastructure, as well as configuration and management of a wide range of network services. This paradigm shift in network operations and usage results in a tighter integrationof the infrastructure with the platform that allows instantiating the media services. The setup of the infrastructure in use for an urban testbed requires operators to carefully plan to acquire equipment with the expected technical characteristics, as well as to meaningfully configure, integrate with the platform on top and setup tools to monitor it. In this paper we share our experience of deploying an infrastructure on the street that supports the FLAME platform. Specifically, we introduce key points to consider when first defining the infrastructure site and equipment, when configuring, operating and monitoring it; and when experiments are to be tested
Location-aware cloud native media service delivery
No full textThis paper discusses the Facility for Large-scale Adaptive Media Experimentation (FLAME) Platform, a Servicebased Architecture that demonstrates the concept of cloud native service orchestration and routing for media applications. This enables automated provisioning and management of microservices delivering a service function chain, which affords considerable flexibility and control to achieve delivery of defined Quality of Service to users in the face of varying demand, while at reasonable cost. The architecture of the system is presented, together with an exemplar media application illustrating automated routing and dynamic, automated and location-based control of microservices. Automated event detection and policies control the scaling of localised edge services, and user requests are automatically routed to local services. Illustrative examples of the performance characteristics on a testbed platform comparing two scenarios - scaled-in (one central server) and scaled-out (local content serving at the edge) - are discussed, as well as the time to switch between the scaling scenarios based on sensed local demand. Results indicate that on the testbed platform, switching between scaled-in and scaled out takes in the order of 2-3 seconds, and the scaled-out scenario has between a four-fold and six-fold user-experience performance gain over the scaled-in scenario
D3.7: FLAME technology roadmap V2
No full textThis report is the second technology roadmap for a ground-breaking media service delivery platform being developed by the FLAME project. The report describes the software products to be delivered at infrastructure, platform and media service layers and how combinations of products are used to exploit the benefits of highly distributed software-defined infrastructures. Each product is described in terms of features, baseline implementation technologies and release schedule. At the core of the roadmap is the FLAME platform that brings together components for orchestration, Service Function Routing, Service Function endpoint management and cross-layer management and control. A systems integration and testing plan describes the DevOps environment including multi-project structure, development workflows and continuous integration processes supported by build, provisioning, configuration and automated testing tools. A software integration infrastructure is designed that replicates a part of the production infrastructures in ways that allow flexible configuration of different cross-component test scenarios. Finally, the downstream staging and production infrastructures are summarised completing the end-to-end DevOps pipeline for efficient and high-quality delivery
Experimental methodology for urban-scale media trials v2: Design tools, processes and devOps infrastructure
No full textWhen deploying a digital media service on the FLAME platform, you are placing it in a responsive environment that reacts (using behaviours you have defined) in real-time to metrics of interest to you. Considering this behaviour as a continuous cycle of optimization in which your target user experience (UX) is negotiated with the most efficient use of available platform resources. This report describes the design tools, processes and DevOps infrastructure for experimentation of services on a high distribution 5G infrastructure including mobile edge computing
D3.3 FLAME platform architecture and infrastructure specification V1
No full textD3.3 provides the first version of the FLAME platform architecture and infrastructure specification. It outlines therefore the blueprint that forms the basis for further specification work in WP3 but more importantly the implementation work in WP4 towards a deployable FLAME platform within the infrastructures of Bristol-Is-Open and Barcelona. D3.3 covers main concepts of importance to the FLAME platform, driving use cases, requirements derived from those use cases as well as the platform components and their interaction
A hierarchical AI-based control plane solution for multi-technology deterministic networks
Get PDFFollowing the Industry 4.0 vision of a full digitization of the industry, time-critical services and applications, allowing network infrastructures to deliver information with determinism and reliability, are becoming more and more relevant for a set of vertical sectors. As a consequence, deterministic network solutions are progressively emerging, albeit they are still bounded to specific technological domains. Even considering the existence of interconnected deterministic networks, the provision of an end-to- end (E2E) deterministic service over them must rely on a specific control plane architecture, capable of seamlessly integrate and control the underlying multi-technology data plane. In this work, we envision such a control plane solution, extending previous works and exploiting several innovations and novel architectural concepts. The proposed control architecture is service-centric, in order to provide the necessary flexibility, scalability, and modularity to deal with a heterogenous data plane. The architecture is hierarchical and encompasses a set of management platforms to interact with specific network technologies overarched by an E2E platform for the management, monitoring, and control of E2E deterministic services. Furthermore, Artificial Intelligence (AI) and Digital Twinning are used to enable network predictability and automation, as well as smart resource allocation, to ensure service reliability in dynamic scenarios where existing services may terminate and new ones may need to be deployed
Empirical analysis of measured 802.11 Receive Signal Strength Values using various Atheros based Mini-PCI cards
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