Mathematical tools and methods for analysis of SDN: A comprehensive survey

Girish, L; Rao, Sridhar K. N.

doi:10.1109/ic3i.2016.7918055

Cited by 15 publications

(5 citation statements)

References 20 publications

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“…In this way it is possible to perform traffic shaping and path optimization based on the application requirements. Such mathematical tools have been presented in a survey on the analysis and modeling of SDN [229].…”

Section: B Realizing the Tactile Internetmentioning

confidence: 99%

Toward Haptic Communications Over the 5G Tactile Internet

Αντωνάκογλου

Steinbach

et al. 2018

IEEE Commun. Surv. Tutorials

220

125

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Section: B Realizing the Tactile Internetmentioning

confidence: 99%

Toward Haptic Communications Over the 5G Tactile Internet

Αντωνάκογλου

Steinbach

et al. 2018

IEEE Commun. Surv. Tutorials

220

125

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show abstract

“…Girish and Rao presented two bases of network performance analysis queuing model and network calculus. Moreover, a comparison review of the verification tools was presented.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…Gong et al 35 presented the analytical review of the mathematical tools, architectures and protocols, applications, and control platforms of the SDN approach. Girish and Rao 36 presented two bases of network performance analysis queuing model and network calculus. Moreover, a comparison review of the verification tools was presented.…”

Section: Related Workmentioning

confidence: 99%

A systematic literature review on formal verification of software‐defined networks

Souri

Norouzi

Asghari

et al. 2019

Trans Emerging Tel Tech

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By raising evolutionary network connections, a software‐defined network (SDN) offers a well‐managed and flexible novel network topology. The SDN efforts are provided to abstract the original network configuration for business applications and web services. Additionally, the SDN modifies the network management policies such as topology policies, deployment policies, the applicability, and infrastructure maintenance. One of the important features of the SDN is standardizing the network interfaces with the precise functional semantics in application and control flow layers. In another hand, specification and evaluation of the existing network interfaces is a main and significant challenge to prove the correctness of the configurable scenarios in the SDN approach. Recently, formal verification presents a high potential platform to evaluate the main‐layer scenarios of the SDN as the novel perspective in this area. Up to now, there is no survey and state‐of‐the‐art review on the formal verification methods in the SDN. This paper provides a systematic literature review (SLR) for the formal verification methods in the SDN in form of the SDN plans to recognize the state of the art of the open challenges. The presented SLR is classified into three main fields: application plan, data plan, and control plan approaches. The verification mechanisms are compared with each other according to the important factors such as structural properties, quality‐of‐service metrics, applied algorithms, and measurement tools.

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“…Machine has to initially think and then learn like a brilliant man. As like a person in society learns from experiences and former data that it is exhibit to and according to that the machine takes decisions in upcoming events [2].…”

Section: A Machine Learning Techniquementioning

confidence: 99%

Crop Yield and Rainfall Prediction in Tumakuru District using Machine Learning

Girish¹

2019

Preprint

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Smart Agriculture is a development that emphasizes the use of information technology in the farming. Most of the population in India depending on agriculture. This situation is one of the reason, that hindering the development of country. Nowadays, even though farmers get more yield for their crop but the market price for that crop will be less, in that case farmers get loss for their product and vice versa. Particularly, when growing new crops, farmers face the risks of both market price and production problems. To overcome these problems, a machine learning technology is used. Predictive analysis is a branch of data mining which predicts the future probabilities and trends. The prediction will help the farmers to choose whether the particular crop is suitable for specific rainfall and crop price values. This approach is to increase the net yield rate of the crop, based on rainfall. Prediction can be carried out by using various machine learning algorithms like linear regression, SVM, K NN method and decision tree algorithm out of which SVM is giving the highest efficiency. The predictive analysis technique can be implemented in several government sectors like APMC, kissan call center etc., by which the government and farmers can get the information of the future rainfall, crop yield and the market price.

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Mathematical tools and methods for analysis of SDN: A comprehensive survey

Cited by 15 publications

References 20 publications

Toward Haptic Communications Over the 5G Tactile Internet

Toward Haptic Communications Over the 5G Tactile Internet

A systematic literature review on formal verification of software‐defined networks

Crop Yield and Rainfall Prediction in Tumakuru District using Machine Learning

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