SOFTWARE INFRASTRUCTURE AND VISUAL ENVIRONMENT OF DISTRIBUTED DATA FLOWS PROCESSING IN SOFTWARE DEFINED NETWORKS

2018 ◽  
Vol 65 ◽  
pp. 44-54
Author(s):  
V. P. Koryachko ◽  
◽  
D. A. Perepelkin ◽  
M. A. Ivanchikova ◽  
V. S. Byshov ◽  
...  
Keyword(s):  
Visual Environment ◽  
Distributed Data ◽  
Software Defined Networks ◽  
Software Infrastructure ◽  
Data Flows

scholarly journals Visual Web-Oriented Environment of Dynamic Control of Data Flow in Campus of Software Defined Networks

2019 ◽  
Vol 26 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Vyacheslav P. Koryachko ◽  
Dmitry A. Perepelkin ◽  
Maria A. Ivanchikova ◽  
Vladimir S. Byshov
Keyword(s):  
Dynamic Control ◽  
Software Test ◽  
Software Systems ◽  
Software Defined Networks ◽  
Campus Network ◽  
Software Infrastructure ◽  
Data Flows ◽  
Uml Class Diagram ◽  
Effective Network ◽  
And Control

Nowadays new innovative approaches based on the technology of software defined networks (SDN) are gaining popularity in the field of computer networks (CN). SDN provide a flexible approach to the processing and control of data flows in CN by separating the control plane and data plane, as well as centralizing the representation of the entire network. In this paper, we propose a software infrastructure and a visual web-oriented environment (SIVE) for dynamic control of data flows in campus SDN based on OpenFlow protocol. It was proposed to use the SIVE as an integrated segment of the campus network of Ryazan State Radio Engineering University. The aim of the work is the development of the SIVE architecture in the form of UML class diagram description, as well as the creation of software methods for organizing effective network interaction of various software systems in SDN based on OpenFlow protocol. A hardware-software test bench based on HP Aruba 2920-24G equipment was developed to confirm the efficiency and reliability of the proposed SIVE. The offered SIVE is the basis for the development of a large class of software systems and SDN components based on OpenFlow protocol.

scholarly journals Aesthetic Archives

2016 ◽  
Vol 41 (1) ◽  
pp. 11-23
Author(s):  
Michael Takeo Magruder ◽  
Jeremy Pilcher
Keyword(s):  
New Media ◽  
Real Time ◽  
Emerging Technologies ◽  
Distributed Data ◽  
Time Data ◽  
The Real ◽  
New Media Art ◽  
Data Flows ◽  
Real Time Data ◽  
Visual Artist

Michael Takeo Magruder, visual artist and researcher, discusses his digital and new media art and practice with Jeremy Pilcher, lawyer and academic, whose research engages with the intersection of art and law. Takeo's work asks viewers to question their relationship both to and within the real-time data flows generated by emerging technologies and the implications these have for archives. His art concerns the way institutions use such systems to create narratives that structure societies. This conversation discusses how Takeo's practice invites us, as individuals, to critically reflect on the implications of the stories that are both told to and about us by using gathered and distributed data.

Bi-objective Traffic Optimization in Geo-distributed Data Flows

2019 ◽  
Vol 16 ◽  
pp. 36-48 ◽  
Author(s):  
Anna-Valentini Michailidou ◽  
Anastasios Gounaris
Keyword(s):  
Distributed Data ◽  
Data Flows ◽  
Traffic Optimization

scholarly journals Semantics for interoperability of distributed data and models: Foundations for better-connected information

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 686 ◽  
Author(s):  
Ferdinando Villa ◽  
Stefano Balbi ◽  
Ioannis N. Athanasiadis ◽  
Caterina Caracciolo
Keyword(s):  
Large Scale ◽  
List Item ◽  
Semantic Annotation ◽  
System Modeling ◽  
High Standard ◽  
List Type ◽  
Distributed Data ◽  
Software Infrastructure ◽  
Conceptual Foundation ◽  
Definition Of

Correct and reliable linkage of independently produced information is a requirement to enable sophisticated applications and processing workflows. These can ultimately help address the challenges posed by complex systems (such as socio-ecological systems), whose many components can only be described through independently developed data and model products. We discuss the first outcomes of an investigation in the conceptual and methodological aspects of semantic annotation of data and models, aimed to enable a high standard of interoperability of information. The results, operationalized in the context of a long-term, active, large-scale project on ecosystem services assessment, include: A definition of interoperability based on semantics and scale;A conceptual foundation for the phenomenology underlying scientific observations, aimed to guide the practice of semantic annotation in domain communities;A dedicated language and software infrastructure that operationalizes the findings and allows practitioners to reap the benefits of data and model interoperability. The work presented is the first detailed description of almost a decade of work with communities active in socio-ecological system modeling. After defining the boundaries of possible interoperability based on the understanding of scale, we discuss examples of the practical use of the findings to obtain consistent, interoperable and machine-ready semantic specifications that can integrate semantics across diverse domains and disciplines.

scholarly journals Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2972
Author(s):  
Marek Amanowicz ◽  
Damian Jankowski
Keyword(s):  
Data Mining ◽  
Network Control ◽  
Flow Rule ◽  
Distributed Data ◽  
Software Defined Networks ◽  
Data Mining Techniques ◽  
Wireless Applications ◽  
Using Data ◽  
Data Centres

The increasing availability of mobile devices and applications, the progress in virtualisation technologies, and advances in the development of cloud-based distributed data centres have significantly stimulated the growing interest in the use of software-defined networks (SDNs) for both wired and wireless applications. Standards-based software abstraction between the network control plane and the underlying data forwarding plane, including both physical and virtual devices, provides an opportunity to significantly increase network security. In this paper, to secure SDNs against intruders’ actions, we propose a comprehensive system that exploits the advantages of SDNs’ native features and implements data mining to detect and classify malicious flows in the SDN data plane. The architecture of the system and its mechanisms are described, with an emphasis on flow rule generation and flow classification. The concept was verified in the SDN testbed environment that reflects typical SDN flows. The experiments confirmed that the system can be successfully implemented in SDNs to mitigate threats caused by different malicious activities of intruders. The results show that our combination of data mining techniques provides better detection and classification of malicious flows than other solutions.

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