Software Defined Cognitive Radio Network Framework

Software defined networking (SDN) provides a novel network resource management framework that overcomes several challenges related to network resources management. On the other hand, Cognitive Radio (CR) technology is a promising paradigm for addressing the spectrum scarcity problem through efficient dynamic spectrum access (DSA). In this paper, the authors introduce a virtualization based SDN resource management framework for cognitive radio networks (CRNs). The framework uses the concept of multilayer hypervisors for efficient resources allocation. It also introduces a semi-decentralized control scheme that allows the CRN Base Station (BS) to delegate some of the management responsibilities to the network users. The main objective of the proposed framework is to reduce the CR users' reliance on the CRN BS and physical network resources while improving the network performance by reducing the control overhead.

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qCon: QoS-Aware Network Resource Management for Fog Computing

Sensors ◽

10.3390/s18103444 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3444 ◽

Cited By ~ 10

Author(s):

Cheol-Ho Hong ◽

Kyungwoon Lee ◽

Minkoo Kang ◽

Chuck Yoo

Keyword(s):

Resource Management ◽

Network Performance ◽

Fog Computing ◽

Raspberry Pi ◽

Small Data ◽

Network Resources ◽

Network Resource ◽

Network Bandwidth ◽

Computing Paradigm ◽

Network Resource Management

Fog computing is a new computing paradigm that employs computation and network resources at the edge of a network to build small clouds, which perform as small data centers. In fog computing, lightweight virtualization (e.g., containers) has been widely used to achieve low overhead for performance-limited fog devices such as WiFi access points (APs) and set-top boxes. Unfortunately, containers have a weakness in the control of network bandwidth for outbound traffic, which poses a challenge to fog computing. Existing solutions for containers fail to achieve desirable network bandwidth control, which causes bandwidth-sensitive applications to suffer unacceptable network performance. In this paper, we propose qCon, which is a QoS-aware network resource management framework for containers to limit the rate of outbound traffic in fog computing. qCon aims to provide both proportional share scheduling and bandwidth shaping to satisfy various performance demands from containers while implementing a lightweight framework. For this purpose, qCon supports the following three scheduling policies that can be applied to containers simultaneously: proportional share scheduling, minimum bandwidth reservation, and maximum bandwidth limitation. For a lightweight implementation, qCon develops its own scheduling framework on the Linux bridge by interposing qCon’s scheduling interface on the frame processing function of the bridge. To show qCon’s effectiveness in a real fog computing environment, we implement qCon in a Docker container infrastructure on a performance-limited fog device—a Raspberry Pi 3 Model B board.

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A Discovery Technology Based on Distributed Resource Management Domain

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.2195 ◽

2015 ◽

Vol 713-715 ◽

pp. 2195-2198

Author(s):

Jun Li Mao ◽

Xiang Luo ◽

Xiao Zhen Wang ◽

Chao Hong Yang

Keyword(s):

Resource Management ◽

Resource Selection ◽

Resource Discovery ◽

Network Resources ◽

Network Resource ◽

Management Domain ◽

Network Resource Management ◽

Long Time ◽

Resource Organization ◽

Resource Description

Resource discovery is the key of network resource management, which includes multiple aspects, such as resource description, resource organization, and resource discovery and resource selection. For a long time, communication network resourcehas been lack of unified and standardized description, causing users difficult to precisely find related resources in demand. This paper presents a distributed resource query methods based on management domain, including distributed resource query architecture, the basic process of resource discovery, update method,query methods and so on. The method of network resources makes use of collaborative queries to realize network resource discovery according to need.

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A Survey of Radio Resource Management in Cognitive Radio Networks

Self-Organization and Green Applications in Cognitive Radio Networks ◽

10.4018/978-1-4666-2812-0.ch013 ◽

2013 ◽

pp. 264-289

Author(s):

Chengshi Zhao ◽

Wenping Li ◽

Jing Li ◽

Zheng Zhou ◽

Kyungsup Kwak

Keyword(s):

Cognitive Radio ◽

Resource Management ◽

Dynamic Spectrum Access ◽

Radio Resource Management ◽

Radio Spectrum ◽

Radio Resource ◽

Green Communications ◽

Radio Resources ◽

Adaptive Power

The framework of “green communications” has been proposed as a promising approach to address the issue of improving resource-efficiency and the energy-efficiency during the utilization of the radio spectrum. Cognitive Radio (CR), which performs radio resource sensing and adaptation, is an emerging technology that is up to the requests of green communications. However, CR networks impose serious challenges due to the fluctuating nature of the available radio resources corresponding to the diverse quality-of-service requirements of various applications. This chapter provides an overview of radio resource management in CR networks from several aspects, namely dynamic spectrum access, adaptive power control, time slot, and code scheduling. More specifically, the discussion focuses on the deployment of CR networks that do not require modification to existing networks. A brief overview of the radio resources in CR networks is provided. Then, three challenges to radio resource management are discussed.

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A Virtual Network Resource Allocation Framework Based on SR-IOV

Applied Sciences ◽

10.3390/app9010137 ◽

2019 ◽

Vol 9 (1) ◽

pp. 137

Author(s):

Zhiyong Ye ◽

Yuanchang Zhong ◽

Yingying Wei

Keyword(s):

Network Performance ◽

Virtual Machines ◽

Dynamic Allocation ◽

Network Resources ◽

Network Resource ◽

Throughput Rate ◽

Network Bandwidth ◽

On Demand ◽

Performance Requirements ◽

And Performance

The workload of a data center has the characteristics of complexity and requirement variability. However, in reality, the attributes of network workloads are rarely used by resource schedulers. Failure to dynamically schedule network resources according to workload changes inevitably leads to the inability to achieve optimal throughput and performance when allocating network resources. Therefore, there is an urgent need to design a scheduling framework that can be workload-aware and allocate network resources on demand based on network I/O virtualization. However, in the current mainstream I/O virtualization methods, there is no way to provide workload-aware functions while meeting the performance requirements of virtual machines (VMs). Therefore, we propose a method that can dynamically sense the VM workload to allocate network resources on demand, and can ensure the scalability of the VM while improving the performance of the system. We combine the advantages of I/O para-virtualization and SR-IOV technology, and use a limited number of virtual functions (VFs) to ensure the performance of network-intensive VMs, thereby improving the overall network performance of the system. For non-network-intensive VMs, the scalability of the system is guaranteed by using para-virtualized Network Interface Cards (NICs) which are not limited in number. Furthermore, to be able to allocate the corresponding bandwidth according to the VM’s network workload, we hierarchically divide the VF’s network bandwidth, and dynamically switch between VF and para-virtualized NICs through the active backup strategy of Bonding Drive and ACPI Hotplug technology to ensure the dynamic allocation of VF. Experiments show that the allocation framework can effectively improve system network performance, in which the average request delay can be reduced by more than 26%, and the system bandwidth throughput rate can be improved by about 5%.

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An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

Sensors ◽

10.3390/s19061395 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1395 ◽

Cited By ~ 8

Author(s):

Stephen Oyewobi ◽

Gerhard Hancke ◽

Adnan Abu-Mahfouz ◽

Adeiza Onumanyi

Keyword(s):

Reinforcement Learning ◽

Spectrum Sensing ◽

Dynamic Spectrum Access ◽

Network Performance ◽

Channel Selection ◽

Selection Strategy ◽

Spectrum Access ◽

Network Resources ◽

Spectrum Handoff ◽

Industrial Internet

The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.

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Artificial Intelligence Control Logic in Next-Generation Programmable Networks

Applied Sciences ◽

10.3390/app11199163 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9163

Author(s):

Mateusz Żotkiewicz ◽

Wiktor Szałyga ◽

Jaroslaw Domaszewicz ◽

Andrzej Bąk ◽

Zbigniew Kopertowski ◽

...

Keyword(s):

Artificial Intelligence ◽

Bandwidth Allocation ◽

Network Performance ◽

Next Generation ◽

Control Logic ◽

Network Resources ◽

Network Resource ◽

Programmable Networks ◽

Network Utilization ◽

Effective Network

The new generation of programmable networks allow mechanisms to be deployed for the efficient control of dynamic bandwidth allocation and ensure Quality of Service (QoS) in terms of Key Performance Indicators (KPIs) for delay or loss sensitive Internet of Things (IoT) services. To achieve flexible, dynamic and automated network resource management in Software-Defined Networking (SDN), Artificial Intelligence (AI) algorithms can provide an effective solution. In the paper, we propose the solution for network resources allocation, where the AI algorithm is responsible for controlling intent-based routing in SDN. The paper focuses on the problem of optimal switching of intents between two designated paths using the Deep-Q-Learning approach based on an artificial neural network. The proposed algorithm is the main novelty of this paper. The Developed Networked Application Emulation System (NAPES) allows the AI solution to be tested with different patterns to evaluate the performance of the proposed solution. The AI algorithm was trained to maximize the total throughput in the network and effective network utilization. The results presented confirm the validity of applied AI approach to the problem of improving network performance in next-generation networks and the usefulness of the NAPES traffic generator for efficient economical and technical deployment in IoT networking systems evaluation.

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Green Networking in Cellular HetNets: A Unified Radio Resource Management Framework With Base Station ON/OFF Switching

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2016.2636455 ◽

2017 ◽

Vol 66 (7) ◽

pp. 5879-5893 ◽

Cited By ~ 20

Author(s):

Hakim Ghazzai ◽

Muhammad Junaid Farooq ◽

Ahmad Alsharoa ◽

Elias Yaacoub ◽

Abdullah Kadri ◽

...

Keyword(s):

Resource Management ◽

Radio Resource Management ◽

Base Station ◽

Radio Resource ◽

Green Networking ◽

Management Framework

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CELLULAR NETWORK RESOURCE DISTRIBUTION METHODS FOR THE JOINT SERVICING OF REAL-TIME MULTISERVICE TRAFFIC AND GROUPED IOT TRAFFIC

T-Comm ◽

10.36724/2072-8735-2020-14-10-61-69 ◽

2020 ◽

Vol 14 (10) ◽

pp. 61-69

Author(s):

Umer Mukhtar Andrabi ◽

◽

Sergey N. Stepanov ◽

Juvent Ndayikunda ◽

Margarita G. Kanishcheva ◽

...

Keyword(s):

Resource Management ◽

Network Performance ◽

Future Internet ◽

Surveillance Systems ◽

Network Resource ◽

Coverage Area ◽

Limited Bandwidth ◽

Massive Number ◽

Key Issues ◽

Iot Devices

Immense growth in the volumes and multiplicity of data to be collected in future Internet of Things (IoT) applications is one of the crucial challenges for the networking organizations as they develop from 4G+ to true 5G systems. Particularly bulk of this traffic includes complex, unstructured and varied data (Big Data) evolve from smart networking ecosystems (LTE-devices, NB-IoT devices). Although 5G offers many low power wide area technologies (Lora WAN, GSM and NB-IoT etc.), principally NB-IoT seems very promising addressing the problem because of its certain characteristics like high fault tolerance, delay tolerance, higher coverage area etc. However, due to the limited bandwidth (180 kHz) availability one of the challenges is how to efficiently use these resources to support and handle massive number of growing IoT devices, also resource management and allocation methodology between LTE and NB-IoT traffic flows. In this context, several key issues for IoT communications in 5G networks should be addressed to satisfy quality of service (QoS) provisioning. In this paper, we proposed a mathematical model for Operator Surveillance systems for sharing radio resources between LTE and NB-IoT. The model utilizes the technique of network slicing for resource management. The proposed techniques provide scenarios that aims to offer a trade-off between the two types of traffics by guaranteeing the network performance and avoiding unproductive utilization of available resources.

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