Ensuring the quality of service flows in multiservice infrastructure based on network node virtualization

Beshley, Mykola; Romanchuk, Vasyl; Червенець, В. В.; Masiuk, Andriy

doi:10.1109/ukrmico.2016.7739624

Cited by 28 publications

(5 citation statements)

References 5 publications

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“…This operation is consistent with memory and fixed storage, and it is a test operation that can control the difficulty. The server can dynamically adjust the time difficulty required for storage component testing by setting the n value to meet the time requirements for computer node identification acquisition in the field of distributed AI collaborative computing [6].…”

Section: Methodsmentioning

confidence: 99%

Research on real-time computing method of computer node identification in the field of scalable and verifiable distributed AI collaborative computing

Zhang¹,

Song²,

Ren

et al. 2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

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The invention relates to a node authentication method and system of a distributed AI collaborative computing system. The method comprises: a central server sends authentication instructions to each distributed node; each distributed node completes the corresponding real-time computing capability verification task according to the authentication instruction and sends a response message to the central server; the response message includes task completion time and node equipment information; the real-time computing capability verification task includes a real-time computing capability verification task and a real-time storage capability verification task; the central server receives the response message. If the time of receiving the response message exceeds the expected time, or the difference between the task completion time and the task completion time of the real node of the same device type completing the real-time computing capability verification task is greater than the preset threshold, the node is identified as a suspicious node; the central server sends authentication instructions to the suspicious node for many times and receives the response message returned by the suspicious node. If the time of receiving the response message exceeds the expected time or the difference between the task completion time and the task completion time of the real node of the same device type completing the real-time computing capability verification task is greater than the preset threshold, Then it is judged that the node is forged.

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Section: Methodsmentioning

confidence: 99%

Research on real-time computing method of computer node identification in the field of scalable and verifiable distributed AI collaborative computing

Zhang¹,

Song²,

Ren

et al. 2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

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“…! "#$%&'()(%30 5!6(%307 Various approaches have been studied for partitioning physical resources, the aim being to improve the flexibility and efficiency of the virtualized networks [32][33][34]. Here, we assume three basic types of interface partitioning ( Table 3).…”

Section: {01}mentioning

confidence: 99%

Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks

Murakami

Kominami

Leibnitz³

et al. 2019

Applied Sciences

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In realizing the network environment assumed by the Internet-of-Things, network slicing has drawn considerable attention as a way to enhance the utilization of physical networks (PNs). Meanwhile, slicing has been shown to cause interdependence among sliced virtual networks (VNs) by propagating traffic fluctuations from one network to others. However, for interconnected networks with mutual dependencies, known as a network of networks (NoN), finding a reliable design method that can cope with environmental changes is an important issue that is yet to be addressed. Some NoN models exist that describe the behavior of interdependent networks in complex systems, and previous studies have shown that an NoN model based on the functional networks of the brain can achieve high robustness, but its application to dynamic and practical systems is yet to be considered. Consequently, this paper proposes the Physical–Virtual NoN (PV-NoN) model assuming a network-slicing environment. This model defines an NoN availability state to deal with traffic fluctuations and interdependence among a PN and VNs. Further, we assume three basic types of interdependence among VNs for this model. Simulation experiments confirm that the one applying complementary interdependence inspired by brain functional networks achieves high availability and communication performance while preventing interference among the VNs. Also investigated is a method for designing a reliable network structure for the PV-NoN model. To this end, the deployment of network influencers (i.e., the most influential elements over the entire network) is configured from the perspective of intra/internetwork assortativity. Simulation experiments confirm that availability or communication performance is improved when each VN is formed assortatively or disassortatively, respectively. Regarding internetwork assortativity, both the availability and communication performance are improved when the influencers are deployed disassortatively among the VNs.

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“…Recent research shows that in the era of data encryption, the system can still classify encrypted traffic, ensuring data security [10]. DPI-based solutions provide a complete picture of network usage, identify subscribers that consume large amounts of traffic, and effectively manage traffic in real time to help create or optimize service offerings, improve service quality, manage service policies, and protect the network and its users [11][12][13]. Typically, DPI systems are installed at the most loaded segments of the network (trunk links and connections to higher networks) and at points where service management is required.…”

Section: Introductionmentioning

confidence: 99%

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

Song

Beshley

Przystupa

et al. 2020

Sensors

Self Cite

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In this paper, to solve the problem of detecting network anomalies, a method of forming a set of informative features formalizing the normal and anomalous behavior of the system on the basis of evaluating the Hurst (H) parameter of the network traffic has been proposed. Criteria to detect and prevent various types of network anomalies using the Three Sigma Rule and Hurst parameter have been defined. A rescaled range (RS) method to evaluate the Hurst parameter has been chosen. The practical value of the proposed method is conditioned by a set of the following factors: low time spent on calculations, short time required for monitoring, the possibility of self-training, as well as the possibility of observing a wide range of traffic types. For new DPI (Deep Packet Inspection) system implementation, algorithms for analyzing and captured traffic with protocol detection and determining statistical load parameters have been developed. In addition, algorithms that are responsible for flow regulation to ensure the QoS (Quality of Services) based on the conducted static analysis of flows and the proposed method of detection of anomalies using the parameter Hurst have been developed. We compared the proposed software DPI system with the existing SolarWinds Deep Packet Inspection for the possibility of network traffic anomaly detection and prevention. The created software components of the proposed DPI system increase the efficiency of using standard intrusion detection and prevention systems by identifying and taking into account new non-standard factors and dependencies. The use of the developed system in the IoT communication infrastructure will increase the level of information security and significantly reduce the risks of its loss.

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Ensuring the quality of service flows in multiservice infrastructure based on network node virtualization

Cited by 28 publications

References 5 publications

Research on real-time computing method of computer node identification in the field of scalable and verifiable distributed AI collaborative computing

Research on real-time computing method of computer node identification in the field of scalable and verifiable distributed AI collaborative computing

Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

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