Load‐Balancing and QoS Based Dynamic Resource Allocation Method for Smart Gird Fiber‐Wireless Networks

Xu, Siya; Qi, Feng; Guo, Shaoyong; Zhou, Guiping; Deng, Wei; Li, Yueyue

doi:10.1049/cje.2019.08.007

Cited by 14 publications

(11 citation statements)

References 17 publications

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“…Aiming at energy saving, energy sensing, avoiding network congestion and other service indicators, the embedding process of virtual network is completed by combining the heuristic solution method based on SDN. In reference [41], a dynamic network resource allocation method based on load balancing and QoS is proposed. In this paper, the author proposes a QoS based scheduling mechanism for VNRs, which can reasonably rank incoming services by calculating the priority of VNRs.…”

Section: Vne Algorithms Based On Differentiated Qosmentioning

confidence: 99%

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

et al. 2021

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The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements.

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Section: Vne Algorithms Based On Differentiated Qosmentioning

confidence: 99%

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

et al. 2021

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“…In [231] Xu et al proposed a method for load balancing and dynamic allocation of resources in Fi-Wi access networks so that it was possible to achieve the QoS requirements necessary for smart grid applications and to optimize networking resources allocation. Simulations were used to evaluate the proposed solution.…”

Section: E Fiber-wireless Networkmentioning

confidence: 99%

“…Fi-Wi/RoF/C-RAN [194], [231]- [235] -Collect and send data concerning consumption and load balancing in advanced metering infrastructure and demand response applications.…”

Section: Telecommunication Technologies Related Work Typical Applicatmentioning

confidence: 99%

Control Networks and Smart Grid Teleprotection: Key Aspects, Technologies, Protocols, and Case-Studies

et al. 2020

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The appeal for more reliable, efficient and resilient power grids has become indispensable with the recent technological development and ever-increasing demand for electrical energy. These goals might be achieved by introducing sensors and actuators in the power grid, with the purpose of enabling a smart control of the power network. All power devices must be connected using a reliable communication network, which should be able to operate even when the power grid fails. Currently, several communication technologies have been applied for supporting this new application scenario. This paper aims for reviewing both academic and market use cases for technologies applied to the mission-critical, control networks and Smart Grid applications, such as teleprotection, self-healing, communication with control centers and field devices, among others. More specifically, the key aspects, potential technologies, main protocols and use cases of operating data networks in energy transmission, power distribution and smart grid environments, including circuit and packet switching technologies, are discussed into details. Furthermore, resilience and the main telecommunications technologies used in power grids, as well, the correlation among them, are exploited in practical point of view. The article provides a broad discussion on the best telecommunication options to build the emerging intelligent energy distribution systems, covering control networks, teleprotection, and smart grid applications.

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“…Wireless network is a typical representative of the above-mentioned networks and has been extremely widely used by virtue of its flexibility of not requiring wired connections [3]. With the rapid development of network virtualization (NV) technology, virtual network architecture has gradually begun to serve as an infrastructure to support the development of wireless networks [4]- [6]. As a basic network, wireless networks are widely used in a variety of artificial intelligence (AI) scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…Since the application of wireless networks, scholars and researchers have fully explored the resource allocation structure and allocation methods under this network. Xu et al [14] proposed a dynamic resource allocation method based on load balancing and QoS. The main purpose of this method was to solve the problem of solidifying the optical fiber wireless access (FiWi) network.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding

Wang

Dong²,

Duan

et al. 2022

Preprint

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The rapid development of virtual network architecture makes it possible for wireless network to be widely used.With the popularity of artificial intelligence (AI) industry in daily life, efficient resource allocation of wireless network has become a problem. Especially when network users request wireless network resources from different management domains, they still face many practical problems. From the perspective of virtual network embedding (VNE), this paper designs and implements a multi-objective optimization VNE algorithm for wireless network resource allocation. Resource allocation in virtual network is essentially a problem of allocating underlying resources for virtual network requests (VNRs). According to the proposed objective formula, we consider the optimization mapping cost, network delay and VNR acceptance rate. VNE is completed by node mapping and link mapping. In the experiment and simulation stage, it is compared with other VNE algorithms, the cross domain VNE algorithm proposed in this paper is optimal in the above three indicators. This shows the effectiveness of the algorithm in wireless network resource allocation.

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Load‐Balancing and QoS Based Dynamic Resource Allocation Method for Smart Gird Fiber‐Wireless Networks

Cited by 14 publications

References 17 publications

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

Control Networks and Smart Grid Teleprotection: Key Aspects, Technologies, Protocols, and Case-Studies

Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding

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