A Survey on Autonomic Provisioning and Management of QoS in SDN Networks

BinSahaq, Ahmed; Sheltami, Tarek R.

doi:10.1109/access.2019.2919957

Cited by 39 publications

(20 citation statements)

References 230 publications

(346 reference statements)

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“…These determined values are used by the monitoring module to manage loads when a new connection is made. When connection usage reaches 80%, a different path is selected instead of the shortest one, to prevent packet loss [16,17]. A survey on managing QoS in SDN Networks [17] involved reviewing various methods to monitor and manage QoS.…”

Section: B Analysis Of Approaches To Network Performance Inmentioning

confidence: 99%

“…When connection usage reaches 80%, a different path is selected instead of the shortest one, to prevent packet loss [16,17]. A survey on managing QoS in SDN Networks [17] involved reviewing various methods to monitor and manage QoS. The authors concluded that there is a lack of extensive analysis on the network performance parameters, including delay, throughput and jitter, particularly when dealing with variety of network applications, such as video, audio, and data, in one testbed framework.…”

Section: B Analysis Of Approaches To Network Performance Inmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Traffic Shaping Algorithm for SDN-Sliced Networks using a New WFQ Technique

Al-Haddad¹,

Velazquez²,

Fatima³

et al. 2021

IJACSA

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Managing traditional networks comes with number of challenges due to their limitations, in particular, because there is no central control. Software-Defined Networking (SDN) is a relatively new idea in networking, which enables networks to be centrally controlled or programmed using software applications. Novel traffic shaping (TS) algorithms are proposed for the implementation of a Quality of Service (QoS) bandwidth management technique to optimise performance and solve network congestion problems. Specifically, two algorithms, namely "Packet tagging, Queueing and Forwarding to Queues" and "Allocating Bandwidth", are proposed for implementing a Weighted Fair Queuing (WFQ) technique, as a new methodology in an SDN-sliced testbed to reduce congestion and facilitate a smooth traffic flow. This methodology aimed at improving QoS that does two things simultaneously, first, making traffic conform to an individual rate using WFQ to make the appropriate queue for each packet. Second, the methodology is combined with buffer management, which decides whether to put the packet into the queue according to the proposed algorithm defined for this purpose. In this way, the latency and congestion remain in check, thus meeting the requirements of real-time services. The Differentiated Service (DiffServ) protocol is used to define classes in order to make network traffic patterns more sensitive to the video, audio and data traffic classes, by specifying precedence for each traffic type. SDN networks are controlled by floodlight controller(s) and FlowVisor, the slicing controller, which characterise the behaviour of such networks. Then, the network topology is modelled and simulated via the Mininet Testbed emulator platform. To achieve the highest level of accuracy, The SPSS statistical package Analysis of Variance (ANOVA) is used to analyse particular traffic measures, namely throughput, delay and jitter as separate performance indices, all of which contribute to QoS. The results show that the TS algorithms do, indeed, permit more advanced allocation of bandwidth, and that they reduce critical delays compared to the standard FIFO queueing in SDN.

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Section: B Analysis Of Approaches To Network Performance Inmentioning

confidence: 99%

Section: B Analysis Of Approaches To Network Performance Inmentioning

confidence: 99%

A Novel Traffic Shaping Algorithm for SDN-Sliced Networks using a New WFQ Technique

Al-Haddad¹,

Velazquez²,

Fatima³

et al. 2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…Similar to work in [64,65], the revenue of serving a VN request is defined by summing up the required CPU and bandwidth resource for VN requests as shown in Equation (7), where α and β are the weights. Although revenue is the metric that InP will gain by accepting VN requests, it is not very considerable without knowing the cost of InP.…”

Section: Profitmentioning

confidence: 99%

“…Network virtualization and orchestration are key processes for network slicing, where software-defined network (SDN) and network function virtualization (NFV) are the key enabling technologies for network orchestration [4,5]. In addition, SDN can manage and deploy the service requirements automatically by decoupling the transmitting layer and control layer [6,7]. It is specified to leverage the benefits of network virtualization to allow high flexibility among various mobile and IoT services in multi-domain heterogeneous converged optical networks.…”

Section: Introductionmentioning

confidence: 99%

Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges

Zong

Feng

Guan

et al. 2020

Sensors

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The emerging 5G applications and the connectivity of billions of devices have driven the investigation of multi-domain heterogeneous converged optical networks. To support emerging applications with their diverse quality of service requirements, network slicing has been proposed as a promising technology. Network virtualization is an enabler for network slicing, where the physical network can be partitioned into different configurable slices in the multi-domain heterogeneous converged optical networks. An efficient resource allocation mechanism for multiple virtual networks in network virtualization is one of the main challenges referred as virtual network embedding (VNE). This paper is a survey on the state-of-the-art works for the VNE problem towards multi-domain heterogeneous converged optical networks, providing the discussion on future research issues and challenges. In this paper, we describe VNE in multi-domain heterogeneous converged optical networks with enabling network orchestration technologies and analyze the literature about VNE algorithms with various network considerations for each network domain. The basic VNE problem with various motivations and performance metrics for general scenarios is discussed. A VNE algorithm taxonomy is presented and discussed by classifying the major VNE algorithms into three categories according to existing literature. We analyze and compare the attributes of algorithms such as node and link embedding methods, objectives, and network architecture, which can give a selection or baseline for future work of VNE. Finally, we explore some broader perspectives in future research issues and challenges on 5G scenario, field trail deployment, and machine learning-based algorithms.

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“…For each state st at time t there is a set A(st) of possible actions. The AI gets either bonuses or punishments for the acts it performs to get the system to do what the programmer wants [89]. The aim is to maximize overall reward.…”

Section: Reinforced Learningmentioning

confidence: 99%

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

et al. 2020

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Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI.

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A Survey on Autonomic Provisioning and Management of QoS in SDN Networks

Cited by 39 publications

References 230 publications

A Novel Traffic Shaping Algorithm for SDN-Sliced Networks using a New WFQ Technique

A Novel Traffic Shaping Algorithm for SDN-Sliced Networks using a New WFQ Technique

Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

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