Software-defined networks for resource allocation in cloud computing: A survey

Mohamed, Arwa; Hamdan, Mosab; Khan, Suleman; Abdelaziz, Ahmed; Babiker, Sharief F.; Imran, Muhammad; Marsono, Muhammad Nadzir

doi:10.1016/j.comnet.2021.108151

Cited by 45 publications

(8 citation statements)

References 142 publications

(135 reference statements)

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“…To achieve enhancement in network performance with minimized delay and improved throughput, SDN and NFV architectures are combined to achieve centralized control on VNFs [ 34 ]. QoS aware virtualization enabled routing (QVR) is implemented to virtualize the routing process in SDN to accomplish less network delay [ 35 ]. Bandwidth Risk Ratio algorithm processes vSDN components based on flexibility factor, available bandwidth, and fault tolerance to maximize profit and meet guaranteed SLA policies [ 36 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Prabakaran

Ramalakshmi

Hussain

et al. 2022

Sensors

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Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller’s role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native–Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.

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Section: Background and Related Workmentioning

confidence: 99%

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Prabakaran

Ramalakshmi

Hussain

et al. 2022

Sensors

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“…Numerical experimental results show that this method improves detection accuracy with acceptable overhead costs. [24] presented an overview of SDN for resource allocation in cloud computing is provided. The authors studied resource allocation in cloud computing based on SDN.…”

Section: -1-literature Review and Analysismentioning

confidence: 99%

Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks

et al. 2022

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The relevance of this type of network is associated with the development and improvement of protocols, methods, and tools to verify routing policies and algorithmic models describing various aspects of SDN, which determined the purpose of this study. The main purpose of this work is to develop specialized methods to estimate the maximum end-to-end delay during packet transmission using SDN infrastructure. The methods of network calculus theory are used to build a model for estimating the maximum transmission delay of a data packet. The basis for this theory is obtaining deterministic evaluations by analyzing the best and worst-case scenarios for individual parts of the network and then optimally combining the best ones. It was found that the developed method of theoretical evaluation demonstrates high accuracy. Consequently, it is shown that the developed algorithm can estimate SND performance. It is possible to conclude the configuration optimality of elements in the network by comparing the different possible configurations. Furthermore, the proposed algorithm for calculating the upper estimate for packet transmission delay can reduce network maintenance costs by detecting inconsistencies between network equipment settings and requirements. The scientific novelty of these results is that it became possible to calculate the achievable upper data delay in polynomial time even in the case of arbitrary tree topologies, but not only when the network handlers are located in tandem. Doi: 10.28991/ESJ-2022-06-05-010 Full Text: PDF

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“…However, the complexity of this mechanism was relatively high, which degrades the performance of network. Mohamad et al 15 conducted a detailed survey on various resource allocation mechanisms used for the SDN architecture. From this paper, it was observed that the parameters such as scalability, visibility, and reliability of network must be ensured for obtaining an improved performance rate.…”

Section: Related Workmentioning

confidence: 99%

An enhanced HFPA‐IHS‐ORP‐based optimization mechanism for efficient bandwidth allocation in software‐defined networks

Paulraj¹,

Rajaram

2022

Int J Communication

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Summary Nowadays, due to the increased amount of network users and resources, some emerging technologies like big data and cloud have been developed. But managing these types of networks is more complex; in order to solve this issue, a software‐defined network (SDN) is developed for managing the network in a comfortable manner, in which maintaining load balance and energy efficiency of network is highly important to satisfy the requirements of quality of service (QoS). For these reasons, various optimization‐based algorithms are developed in the traditional works, but these lack with the limitations of increased network traffic and energy consumption. In order to solve these issues, an enhanced hybrid flower pollination algorithm (HFPA) with improved harmony search (IHS) is developed in this paper. It intends to obtain an improved resource utilization and reduced network load and delay by efficiently allocating the bandwidth to the network. Moreover, an optimal routing protocol (ORP) is employed to perform the data transmission with condensed network traffic. During experimentation, the performance results of the proposed algorithm are examined and compared with some traditional methodologies. From the results, it is noted that the proposed HFPA‐IHS‐ORP provides the better results than the existing methods.

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Software-defined networks for resource allocation in cloud computing: A survey

Cited by 45 publications

References 142 publications

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks

An enhanced HFPA‐IHS‐ORP‐based optimization mechanism for efficient bandwidth allocation in software‐defined networks

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