Assessment of QoS adaptation and cyber-defense mechanisms in networked systems

Ravindran, K.; Wardei, Yassine; Kodia, A.; Iannelli, Michael; Adiththan, Arun; Drager, Steven

doi:10.1109/desec.2017.8073833

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Quality of Service (QoS) [19] adaptation in network slicing is critical for meeting diverse application needs. Studies have explored mechanisms like Squatting and Kicking Strategies (SKM) for resource utilization [20], neural network-based user adaptation to QoS policies [21], hybrid deep learning for network reconfiguration [22], per-user QoS guarantees [23], and Morphnet for network management [24]. These approaches are crucial for maintaining QoS in network slices.…”

Section: Qos Adaptation Approachesmentioning

confidence: 99%

Optimizing 6G Network Slicing with the EvoNetSlice Model for Dynamic Resource Allocation and Real-Time QoS Management

Changala

et al. 2024

Int. Res. J. multidiscip. Technovation

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This research paper focuses on thoroughly examining the challenges in 6G network slicing. To develop, evaluate performance characteristics for on-demand reallocation and instantaneously changeable QoS EvoNetSlice model. The study employs integrated evolutionary algorithms with artificial intelligence-enabled data analytics and multi-objective optimization to optimize network resources usage under minimum end-to-end delay, high transmission rates and optimal background data management. Firstly, the network resource allocation individuals should be based on the network traffic data, QoD (quality of demand) value for some applications and users’ behaviors. The performance degradation detection and quality of service (QoS) adaptation mechanism combined with a multi-layer objective fitness function for achieving good balance in conflict between conflicting objectives. Results indicate that EvoNetSlice improves the general efficiency of a particular network, adapts according to ever shifting requirements for QoS at any time and provides crucial statistics-focused data on network management. The importance of this work lies in developing the future 6G network’s technology. W the key issues, including resource optimization and real-time adaptation required to support modern 6G services, are considered by EvoNetSlice. Such an exploration is an essential element in developing flexible 6G systems that will define next-generation wireless communication.

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Section: Qos Adaptation Approachesmentioning

confidence: 99%