5Growth: An End-to-End Service Platform for Automated Deployment and Management of Vertical Services over 5G Networks

Li, Xi; García‐Saavedra, Andrés; Costa‐Pérez, Xavier; Bernardos, Carlos J.; Guimarães, Carlos; Antevski, Kiril; Mangues‐Bafalluy, Josep; Baranda, Jorge; Zeydan, Engin; Corujo, Daniel; Iovanna, Paola; Landi, Giada; Alonso, Jesús Villadangos; Paixão, Paulo; Martins, Hugo; Lorenzo, Manuel Ferraz; Ordoñez-Lucena, José; López, Diego

doi:10.1109/mcom.001.2000730

Cited by 71 publications

(36 citation statements)

References 7 publications

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“…This subsection presents a quantitative evaluation of the performance of the presented AC algorithms in a realistic testbed environment (EXTREME Testbed) using the 5Growth platform [18]. The modular and flexible architecture of the 5Growth platform allows the straightforward integration of various AC techniques.…”

Section: E Experimental Evaluation Resultsmentioning

confidence: 99%

“…Existing AC solutions were mainly evaluated in simulation environments that raise questions about their performance and efficiency in real networks. In this paper, besides in extensive simulations, the proposed solution is evaluated in a realistic environment using the publicly available 5Growth platform 1 [18], an NFV/SDN-based orchestration framework with AI/ML capabilities to perform closed-loop automation and zero-touch service and network management.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Provider NFV Network Service Delegation via Average Reward Reinforcement Learning

Bakhshi¹,

Mangues-Bafalluy²,

Baranda³

2021

Preprint

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In multi-provider 5G/6G networks, service delegation enables administrative domains to federate in provisioning NFV network services. Admission control is fundamental in selecting the appropriate deployment domain to maximize average profit without prior knowledge of service requests' statistical distributions. This paper analyzes a general federation contract model for service delegation in various ways. First, under the assumption of known system dynamics, we obtain the theoretically optimal performance bound by formulating the admission control problem as an infinite-horizon Markov decision process (MDP) and solving it through dynamic programming. Second, we apply reinforcement learning to practically tackle the problem when the arrival and departure rates are not known. As Q-learning maximizes the discounted rewards, we prove it is not an efficient solution due to its sensitivity to the discount factor. Then, we propose the average reward reinforcement learning approach (R-Learning) to find the policy that directly maximizes the average profit. Finally, we evaluate different solutions through extensive simulations and experimentally using the 5Growth platform. Results confirm that the proposed R-Learning solution always outperforms Q-Learning and the greedy policies. Furthermore, while there is at most 9% optimality gap in the ideal simulation environment, it competes with the MDP solution in the experimental assessment.

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Section: E Experimental Evaluation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Provider NFV Network Service Delegation via Average Reward Reinforcement Learning

Bakhshi¹,

Mangues-Bafalluy²,

Baranda³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The essential communications of 5G networks are now available in real-time for the first time. It implies that 5G may be deployed wirelessly with apps that are fully reliable and secure to run (Aijaz, 2020;Li et al, 2021aLi et al, , 2021b. This also applies to a remotely operated production facility in Industry 4.0.…”

Section: G Technologies and Types Of Network Used In 5gmentioning

confidence: 99%

“…There are varieties of structures that support 5G for Industry 4.0 applications, namely, mobility, wireless communications, VR and AR, facility for remote video surveillance, remote tracking, etc. The above-highlighted features will enable the Industry 4.0 culture throughout the industrial sectors (Marabissi et al, 2019;Li et al, 2021aLi et al, , 2021b.…”

Section: Associated Features and Specialties Of 5g For Industry 40mentioning

confidence: 99%

Progressive schema of 5G for Industry 4.0: features, enablers, and services

Javaid

Haleem

Rab

et al. 2022

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Purpose The development of new communication technology such as 5G is now a solid choice for Industry 4.0. 5G in the fifth generation replaces the presently used mobile telecommunications networks. In every sector of modern life, this technology is designed to resolve the issue of the enormous rise in daily gadgets. 5G allows rapid data transfer and significantly improves the Internet of Things (IoT), which contains billions of devices. The purpose of this paper is to study the potential applications of 5G for Industry 4.0. Design/methodology/approach Relevant research publications from Scopus, Web of Science and Google Scholar were identified to accomplish the objectives of this paper. 5G for Industry 4.0 enables crucial communications such as wireless management of machines and robotics to unleash Industry 4.0’s full potential, including the considerable proliferation of IoT devices in 5G. This technology enhances quality control, increasing inspections that rely on real-time analysis for rapid and early detection. Findings In Industry 4.0, 5G technology provides high speed and flexibility connectivity which helps to enhance the entire manufacturing system. This paper briefs about 5G and different network technologies used in 5G. Advancements, associated features and specialties, and significant enablers of 5G for Industry 4.0 are discussed. Finally, the paper identifies and discusses eighteen 5G applications for Industry 4.0. 5 G-enabled robots are used in manufacturing to carry out extensive work to enhance connectivity. Originality/value 5G is the next-generation communication technology, allowing numerous examples of inventive usage, including Industry 4.0. In line with its long-term vision of digitisation, 5G benefit the whole value chain, including consumers and businesses.

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“…On the other hand, the multiplicity of technology-specific orchestration platforms presents interoperability challenges, which motivates current research in 5G and B5G networks towards delivery of E2E Network Slicing solutions that support the orchestration and management of lheterogeneous resources in distributed edge to cloud infrastructures and across the different network segments [28], i.e., radio, core, and transport. In addition, although there are solutions that optimize the hardware (optimized design of dedicated solutions) or the job allocation in the computing nodes, they provide local optimization benefits, while overlooking the holistic network optimization.…”

Section: Research Gapmentioning

confidence: 99%

Offline Joint Network and Computational Resource Allocation for Energy-Efficient 5G and beyond Networks

et al. 2021

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In order to cope with the ever-increasing traffic demands and stringent latency constraints, next generation, i.e., sixth generation (6G) networks, are expected to leverage Network Function Virtualization (NFV) as an enabler for enhanced network flexibility. In such a setup, in addition to the traditional problems of user association and traffic routing, Virtual Network Function (VNF) placement needs to be jointly considered. To that end, in this paper, we focus on the joint network and computational resource allocation, targeting low network power consumption while satisfying the Service Function Chain (SFC), throughput, and delay requirements. Unlike the State-of-the-Art (SoA), we also take into account the Access Network (AN), while formulating the problem as a general Mixed Integer Linear Program (MILP). Due to the high complexity of the proposed optimal solution, we also propose a low-complexity energy-efficient resource allocation algorithm, which was shown to significantly outperform the SoA, by achieving up to 78% of the optimal energy efficiency with up to 742 times lower complexity. Finally, we describe an Orchestration Framework for the automated orchestration of vertical-driven services in Network Slices and describe how it encompasses the proposed algorithm towards optimized provisioning of heterogeneous computation and network resources across multiple network segments.

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5Growth: An End-to-End Service Platform for Automated Deployment and Management of Vertical Services over 5G Networks

Cited by 71 publications

References 7 publications

Multi-Provider NFV Network Service Delegation via Average Reward Reinforcement Learning

Multi-Provider NFV Network Service Delegation via Average Reward Reinforcement Learning

Progressive schema of 5G for Industry 4.0: features, enablers, and services

Offline Joint Network and Computational Resource Allocation for Energy-Efficient 5G and beyond Networks

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