Network virtualization with dynamic resource pooling and trading mechanism

Xie, Weisheng; Zhu, Jiafeng; Huang, Changcheng; Luo, Min; Chou, Wu

doi:10.1109/glocom.2014.7037074

Cited by 17 publications

(6 citation statements)

References 13 publications

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“…To address this issue, there are some studies that have considered resource trading in the context of network slicing. In [12], Xie et al proposed a dynamic resource pooling and trading mechanism in network virtualization using Stackelberg game to achieve more efficient utilization of network resources. In [13,14], Akguel et al proposed an algorithm for trading resources among tenants.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Resource Management for Network Slicing

Kim

Lim

2019

Applied Sciences

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Network slicing to create multiple virtual networks, called network slice, is a promising technology to enable networking resource sharing among multiple tenants for the 5th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant's point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Resource Management for Network Slicing

Kim

Lim

2019

Applied Sciences

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“…Although the network service provider determines the number of virtual nodes' CPU and the bandwidth of virtual links, users are the real consumers that utilize network resources. Most of current research work on VNE adopt excessive allocation, where user's requested resources are equal to its peak demands (as shown in Figure ) …”

Section: Related Workmentioning

confidence: 99%

An RBF neural network–based dynamic virtual network embedding algorithm

Zheng

Zhang

et al. 2018

Concurrency and Computation

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Summary Network virtualization is a promising technology, which is used to solve the existing shortcomings of the Internet architecture and in which virtual network (VN) embedding plays a vital role in resource allocation. However, current virtual network embedding algorithms mainly deal with static VN embedding; the allocations of network resources are constant and excessive in the VN requests' lifetime. In reality, the user's resource requirements usually change with time; hence, static VN embedding algorithms can lead to low utilization of substrate resources and reduction of the operators' revenues. To solve the aforementioned problem, we focus on the virtual network embedding considering dynamic demands in this paper. We apply supervised radial basis function (RBF) neural network to predict the changes of virtual requests and dynamically reallocate substrate resources by adjusting already allocated resources. Simulation experiments show that compared with the static algorithms, dynamic VN embedding schemes with supervised RBF can more accurately predict the demand changes of virtual networks, which adaptively adjusts resources allocation and management and therefore improves resource utilization of substrate networks.

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“…Resource pooling is a general principle based on making a collection of resources behave like a single pooled resource . Applied to networks, this principle aims to increase their reliability, flexibility, and efficiency.…”

Section: Virtual Network Resource Poolingmentioning

confidence: 99%

“…Resource pooling is a general principle based on making a collection of resources behave like a single pooled resource. 6 Applied to networks, this principle aims to increase their reliability, flexibility, and efficiency. While it has been investigated in many forms, a large amount of the related research effort has been put into transport layer protocols.…”

Section: Virtual Network Resource Poolingmentioning

confidence: 99%

A hybrid algorithm to combine redundancy and concurrency in virtual network resource pooling

Klapez

Grazia

Casoni

2017

Internet Technology Letters

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Emergency scenarios pose significant hurdles to wireless communications, especially when these are performed through terrestrial cellular networks. This is because network infrastructures can be damaged, or the sudden surge of network demand can cause unbearable network congestion. Unfortunately, public safety operators employ terrestrial cellular networks to perform data communications and are therefore at risk of being unable to effectively operate in critical situations. Virtual network resource pooling is a software‐defined networking (SDN)‐based network framework that allows collaborative hosts to pool together their network channels to provide connectivity despite the sudden unavailability of network paths or to compensate for high degrees of packet losses. Once hosts' resources are pooled, a dispatching algorithm on the SDN controller is required to enforce a proper policy of packets distribution. This paper presents a dispatching algorithm that is designed to provide fast and reliable transmissions despite lossy and unreliable channels. It works by introducing the minimum amount of packet redundancy that is needed to obtain a packet delivery probability given as a parameter and by using the remaining network resources to augment transfer goodput. Emulation results confirm that with respect to policies that selectively aim to improve goodput or to introduce packet redundancy, this hybrid algorithm can compensate for high packet loss ratios and improve goodput at the same time.

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Network virtualization with dynamic resource pooling and trading mechanism

Cited by 17 publications

References 13 publications

Reinforcement Learning Based Resource Management for Network Slicing

Reinforcement Learning Based Resource Management for Network Slicing

An RBF neural network–based dynamic virtual network embedding algorithm

A hybrid algorithm to combine redundancy and concurrency in virtual network resource pooling

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