Programmable Edge-to-Cloud Virtualization for 5G Media Industry: The 5G-MEDIA Approach

Rizou, Stamatia; Athanasoulis, Panagiotis; Andriani, Pasquale; Iadanza, Francesco; Trakadas, Panagiotis; Griffin, David; Kheirkhah, Morteza; Breitgand, David; Weit, Avi; Üstok, Refik Fatih; Keskin, Selçuk; Moscatelli, Francesca; Bernini, Giacomo; Macher, Gordana; Serrano, Javier; Jiménez, David

doi:10.1007/978-3-030-49190-1_9

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…In the next few years, network operators will tend towards cloud architectures [107] in both edge and core network [108], to increase efficiency, reliability, and scalability. Cloudnative is the process of transitioning software deployments from traditional infrastructure to software and API-enabled infrastructure to leverage automation and DevOps techniques.…”

Section: Cnf Placementmentioning

confidence: 99%

VNF and CNF Placement in 5G: Recent Advances and Future Trends

Attaoui¹,

Sabir

Elbiaze

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for the most of mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, called Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network's performance, reliability, and operating costs. The VNF placement is NP-Hard. Therefore, there is a need for placement methods that can cope with the complexity of the problem and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include microservices, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of Containers Network Functions (CNFs) in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.

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Section: Cnf Placementmentioning

confidence: 99%

VNF and CNF Placement in 5G: Recent Advances and Future Trends

Attaoui¹,

Sabir

Elbiaze

et al. 2023

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

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“…While today numerous proprietary and open source technical tools support IoT-to-edge-to-cloud scenarios as a single commodity, solutions that are able to truly bring computation and intelligence closer to the edge nodes, where the data are generated, are still facing several technical, societal and business barriers [8]. The main complication regarding this transition is that current solutions solely focus on the hierarchical orchestration of resources, services and data, necessitating data migration from IoT-to-edge-to-cloud, and vice versa [9][10][11][12]. This hierarchical architectural model inevitably results in predefined topologies that exhibit several limitations (see Section 2), thus becoming the main blocking point in implementing the next generation of edge intelligence applications.…”

Section: Introductionmentioning

confidence: 99%

A Reference Architecture for Cloud–Edge Meta-Operating Systems Enabling Cross-Domain, Data-Intensive, ML-Assisted Applications: Architectural Overview and Key Concepts

Trakadas

Masip-Bruin

Facca

et al. 2022

Sensors

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Future data-intensive intelligent applications are required to traverse across the cloud-to-edge-to-IoT continuum, where cloud and edge resources elegantly coordinate, alongside sensor networks and data. However, current technical solutions can only partially handle the data outburst associated with the IoT proliferation experienced in recent years, mainly due to their hierarchical architectures. In this context, this paper presents a reference architecture of a meta-operating system (RAMOS), targeted to enable a dynamic, distributed and trusted continuum which will be capable of facilitating the next-generation smart applications at the edge. RAMOS is domain-agnostic, capable of supporting heterogeneous devices in various network environments. Furthermore, the proposed architecture possesses the ability to place the data at the origin in a secure and trusted manner. Based on a layered structure, the building blocks of RAMOS are thoroughly described, and the interconnection and coordination between them is fully presented. Furthermore, illustration of how the proposed reference architecture and its characteristics could fit in potential key industrial and societal applications, which in the future will require more power at the edge, is provided in five practical scenarios, focusing on the distributed intelligence and privacy preservation principles promoted by RAMOS, as well as the concept of environmental footprint minimization. Finally, the business potential of an open edge ecosystem and the societal impacts of climate net neutrality are also illustrated.

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