Application of Graph Theory Concepts in Computer Networks and its Suitability for the Resource Provisioning Issues in Cloud Computing-A Review

Devi, R. Kanniga; Murugaboopathi, G.

doi:10.3844/jcssp.2018.163.172

Cited by 10 publications

(9 citation statements)

References 67 publications

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“…In a social context, AI could provide useful tools for discovering new social phenomena and for testing existing theories. The basis for such AI systems can be found in the computational graph theory, which was proven to work well in computer networks [111].…”

Section: Artificial Intelligence: Machine Learning (Ml) and Knowledgementioning

confidence: 99%

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

2019

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This paper presents a potential solution to fill a gap in both research and practice that there are few interactions between transnational industry cooperation (TIC) and transnational university cooperation (TUC) in transnational innovation ecosystems. To strengthen the synergies between TIC and TUC for innovation, the first step is to match suitable industrial firms from two countries for collaboration through their common connections to transnational university/academic partnerships. Our proposed matching solution is based on the integration of social science theories and specific artificial intelligence (AI) techniques. While the insights of social sciences, e.g., innovation studies and social network theory, have potential to answer the question of why TIC and TUC should be looked at as synergetic entities with elaborated conceptualization, the method of machine learning, as one specific technic off AI, can help answer the question of how to realize that synergy. On the way towards a transdisciplinary approach to TIC and TUC synergy building, or creating transnational university-industry co-innovation networks, the paper takes an initial step by examining what the supports and gaps of existing studies on the topic are, and using the context of EU–China science, technology and innovation cooperation as a testbed. This is followed by the introduction of our proposed approach and our suggestions for future research.

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Section: Artificial Intelligence: Machine Learning (Ml) and Knowledgementioning

confidence: 99%

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

2019

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“…sw3, sw4, and sw5 have five active ports each, and sw6 to sw14 have four active ports each. The total number of threads equals 57, as computed using eq (1). After executing Request Ports Statistics, Parse JSON, and Access and Extract Traffic Counter Information (processes 6, 7, and 8), traffic counter values are retrieved simultaneously.…”

Section: Case Studymentioning

confidence: 99%

“…Recently, critical data challenges depend on relationships, not just categorizing discrete data. Graph analytics and technologies provide practical tools for connected data within real-world systems, from social networks, protein interactions, power grids, and telecommunication networks, to name a few [1]. As systems become increasingly complicated and data growingly interconnected, it's fundamental to benefit from the evolving and rich relationships within data.…”

Section: Introductionmentioning

confidence: 99%

BG-SDN: A Big Graph Framework for Topology Modeling and Traffic Analysis in Large-Scale SDN

Samaan

Jeiad

2022

Preprint

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This paper presented a new method of modeling a Software Defined Networking (SDN) network as a graph and importing traffic data into the created graph using graph techniques to speed up the computations required in traffic analysis and management. For this purpose, a specialized application named TMTA (Topology Modeling and Traffic Analysis) is created to model the SDN network topology as a graph and feed the created graph with traffic data. To achieve high performance, the TMTA application is integrated with the Neo4j graph database utilizing its ability to store and process big graphs consisting of massive amounts of highly interconnected data in a scalable and reliable manner. For performance evaluation, various sizes of network topologies ranging from 41 to 62,586 devices are modeled using the TMTA application and stored in Neo4j. Evaluation results show that the proposed big graph framework can handle large-scale topologies effectively and perform traffic analysis tasks, i.e., calculating the control score of each device in the network and finding the shortest path between devices in a very reasonable time.

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“…The authors in [11] sequentially construct three different graphs on the basis of an unconventional mapping, conjectured to be a one-way trapdoor function and designed specifically for graph structures. Some work focuses on the application of graph theory principles in computer networks and its potential to tackle the challenges of provisioning in secure cloud computing environments [12]. Two graph based public key cryptosystems have been suggested to secure sensitive Data in the work of Sensarma et al [13], where one is based purely on the properties of matrices, while the other is based on graph codes.…”

Section: Related Workmentioning

confidence: 99%

Data Security: A New Symmetric Cryptosystem based on Graph Theory

Bekkaoui¹,

Ziti²,

Omary³

2021

IJACSA

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Sharing private data in an unsecured channel is extremely critical, as unauthorized entities can intercept it and could break its privacy. The design of a cryptosystem that fulfills the security requirements in terms of confidentiality, integrity and authenticity of transmitted data has therefore become an unavoidable imperative. Indeed, a lot of work has been carried out in this regard. Although many cryptosystems have been proposed in the published literature, it has been found that their robustness and performance vary relatively from one to another. Adopting this reflection, we address in this paper the concept of block cipher, which is a major cryptographic solution to guarantee confidentiality, by involving the properties of graph theory to represent the plaintext message. Our proposal is in fact a new symmetric encryption block cipher that proceeds by representing plaintext messages using disjoint Hamiltonian circuits and then dealing with them as an adjacency matrix in a pre-encryption phase. The proposed system relies on a particular sub-key generator that has been carefully designed to produce the encryption keys according to the specifications of the system. The obtained experimental results demonstrate that our proposed cryptosystem is robust against statistical attacks, particularly the DIEHARD test, and presents both good confusion and good diffusion.

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Application of Graph Theory Concepts in Computer Networks and its Suitability for the Resource Provisioning Issues in Cloud Computing-A Review

Cited by 10 publications

References 67 publications

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

BG-SDN: A Big Graph Framework for Topology Modeling and Traffic Analysis in Large-Scale SDN

Data Security: A New Symmetric Cryptosystem based on Graph Theory

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