Micro Operator Design Pattern in 5G SDN/NFV Network

Tseng, Chia Wei; Huang, Yu; Tseng, Fan Hsun; Yang, Yuxiang; Liu, Chien Chang; Chou, Li-Der

doi:10.1155/2018/3471610

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Tseng et al [34] integrated the hypervisor technique with container virtualization and constructed an integrated virtualization (IV) fog platform for deploying industrial applications based on the virtual network function. Tseng et al [35] addressed the design pattern of the 5G micro operator and proposed a Decision Tree-Based Flow Redirection (DTBFR) mechanism to redirect the traffic flows to neighbor service nodes. e DTBFR mechanism allows different μOs to share network resources and speed up the development of edge computing in the future.…”

Section: Bandwidth Utilization Offloading and Balancingmentioning

confidence: 99%

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Chiu

Liu

Chou

2022

Wireless Communications and Mobile Computing

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The increasing quality and various requirements of network services are guaranteed because of the advancement of the emerging network paradigm, software-defined networking (SDN), and benefits from the centralized and software-defined architecture. The SDN not only facilitates the configuration of the network policies for traffic engineering but also brings convenience for network state obtainment. The traffic of numerous services is transmitted within a network, whereas each service may demand different network metrics, such as low latency or low packet loss rate. Corresponding quality of service policies must be enforced to meet the requirements of different services, and the balance of link utilization is also indispensable. In this research, Reinforcement Discrete Learning-Based Service-Oriented Multipath Routing (RED-STAR) has been proposed to understand the policy of distributing an optimal path for each service. The RED-STAR takes the network state and service type as input values to dynamically select the path a service must be forwarded. Custom protocols are designed for network state obtainment, and a deep learning-based traffic classification model is also integrated to identify network services. With the differentiated reward scheme for every service type, the reinforcement learning model in RED-STAR gradually achieves high reward values in various scenarios. The experimental results show that RED-STAR can adopt the dynamic network environment, obtaining the highest average reward value of 1.8579 and the lowest average maximum bandwidth utilization of 0.3601 among all path distribution schemes in a real-case scenario.

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Section: Bandwidth Utilization Offloading and Balancingmentioning

confidence: 99%

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Chiu

Liu

Chou

2022

Wireless Communications and Mobile Computing

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“…Virtualized cellular networks without OTA testing have been studied widely [7], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25]. OTA testbeds without MEC have been installed on LTE [26], [27], [28], [29] and, to avoid regulated spectrum, on WiFi [30], [31].…”

Section: Related Workmentioning

confidence: 99%

Open-source RANs in Practice: an Over-The-Air Deployment for 5G MEC

Haavisto

Arif

Lovén

et al. 2019

2019 European Conference on Networks and Communications (EuCNC)

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Edge computing that leverages cloud resources to the proximity of user devices is seen as the future infrastructure for distributed applications. However, developing and deploying edge applications, that rely on cellular networks, is burdensome. Such network infrastructures are often based on proprietary components, each with unique programming abstractions and interfaces. To facilitate straightforward deployment of edge applications, we introduce open-source software (OSS) based radio access network (RAN) on over-the-air (OTA) commercial spectrum with Development Operations (DevOps) capabilities. OSS allows software modifications and integrations of the system components, e.g., Evolved Packet Core (EPC) and edge hosts running applications, required for new data pipelines and optimizations not addressed in standardization. Such an OSS infrastructure enables further research and prototyping of novel end-user applications in an environment familiar to software engineers without telecommunications background. We evaluated the presented infrastructure with end-to-end (E2E) OTA testing, resulting in 7.5MB/s throughput and latency of 21ms, which shows that the presented infrastructure provides low latency for edge applications.

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“…1. illustrates an example of integrating SDN and NFV technologies for implementing slicing in 5G communication networks [9].…”

Section: Introductionmentioning

confidence: 99%

Conditionally Infinite Telecommunication Resource for Subscribers

Globa

Skulysh

Siemens

2020

Advances in Information and Communication Technology and Systems

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Modern communication systems should provide the end user with a conditionally infinite telecommunication resource. To ensure guaranteed quality, services are combined into groups. A group of similar services with the same service process requirements is called a slice. Using software-defined networks (SDN) allows to deploy a management system slices and telecommunications resources that are allocated for their maintenance. This is possible only using the latest telecommunication technologies, and seamless user connection management. The article proposes an original methodology for managing the servicing streams process in network service providers tunnels. The methodology uses the allowable service node load calculation, overload prediction and live migration algorithms to seamlessly change the service node for the flow.

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Micro Operator Design Pattern in 5G SDN/NFV Network

Cited by 6 publications

References 29 publications

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Open-source RANs in Practice: an Over-The-Air Deployment for 5G MEC

Conditionally Infinite Telecommunication Resource for Subscribers

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