Autonomous Intelligent VNF Profiling for Future Intelligent Network Orchestration

Ferdosian, Nasim; Moazzeni, Shadi; Jaisudthi, Pratchaya; Ren, Yifei; Agrawal, Himanshu; Simeonidou, Dimitra; Nejabati, Reza

doi:10.1109/tmlcn.2023.3294876

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Regarding our contribution to the field, the Novel Autonomous Profiling (NAP) method [2] focuses on offline autonomous profiling by identifying the initial optimal resource configuration for each standalone VNF based on a weighted resource configuration selection approach. Our recent work of [22] introduces a novel autonomous temporal profiling technique, examining VNF behaviour across performance and resource utilisation aspects. The proposed technique automates profiling, encompassing diverse resource types like computation, memory, and network resources, to yield deeper insight into VNFs resource-performance correlations.…”

Section: State Of the Artmentioning

confidence: 99%

iOn-Profiler: Intelligent Online Multi-Objective VNF Profiling With Reinforcement Learning

Vasilakos,

Moazzeni,

Bravalheri

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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Leveraging the potential of Virtualised Network Functions (VNFs) requires a clear understanding of the link between resource consumption and performance. The current state of the art tries to do that by utilising machine learning and specifically Supervised Learning (SL) models for given network environments and VNF types assuming single-objective optimisation targets. Taking a different approach, iOn-Profiler poses a novel VNF profiler optimising multi-resource type allocation and performance objectives using adapted Reinforcement Learning (RL). Our approach can meet key performance indicator targets while minimising multi-resource type consumption and optimising the VNF output rate compared to existing singleobjective solutions. Our experimental evaluation with three realworld VNF types over a total of 39 study scenarios (13 per VNF), for three resource types (virtual CPU, memory, and network link capacity), verifies the accuracy of resource allocation predictions and corresponding successful profiling decisions via a benchmark comparison between our RL model and SL models. We also conduct a complementary exhaustive search-space study revealing that different resources impact performance in varying ways per VNF type, implying the necessity of multi-objective optimisation, individualised examination per VNF type, and adaptable online profile learning, such as with the autonomous online learning approach of iOn-Profiler.

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Section: State Of the Artmentioning

confidence: 99%

iOn-Profiler: Intelligent Online Multi-Objective VNF Profiling With Reinforcement Learning

Vasilakos,

Moazzeni,

Bravalheri

et al. 2024

IEEE Trans. Netw. Serv. Manage.

Self Cite

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“…In [10], the authors proposed a multi-task cooperative computing mechanism for overload protection in 6G networks, which focuses on node selection and path optimization to improve the efficiency of computational task execution and quality of service by optimizing the delay-aware multi-task cooperative computing objective. In [29], the authors proposed a novel profile-based data-driven VNF performance-resource analysis (PDPA) framework that analyzes the complex relationship between network performance KPIs, resource allocation, and utilization to achieve assurance for next-generation networks. In [30], the authors proposed deep reinforcement learning based on migration techniques to explore network slicing, guaranteeing the normal operation of the network under extreme conditions, avoiding unstable system performance, and violating network service level agreements (SLAs).…”

Section: Related Workmentioning

confidence: 99%

Task Offloading Scheme for Survivability Guarantee Based on Traffic Prediction in 6G Edge Networks

Sun,

Yang,

et al. 2023

Electronics

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With the development of sixth-generation (6G) mobile networks, the rise of emerging intelligent services has led to a huge increase in traffic. As an important technology to support the development of 6G, mobile edge computing (MEC) effectively meets the ultra-low latency requirements of most emerging services. However, due to the limited processing capacity of edge nodes, overload on any edge node will cause service degradation, interruption, and even node failure, weakening the advantages of MEC and reducing the survivability of the whole network. In this paper, we propose a task offloading scheme based on traffic prediction for node-overload protection to ensure the survivability of the 6G edge networks. We transformed the network survivability guarantee problem into a task offloading problem under the constraint of future available resources based on traffic prediction and developed a particle swarm optimization algorithm based on policy gradient (PSO-PG) to jointly optimize offloading decisions, routing, and computing resource allocation. Simulations verify the effectiveness of our proposed scheme and guarantee the survivability of 6G edge networks. Meanwhile, evaluation in multiple scenarios with different node scales has verified the wide applicability of our work.

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Autonomous Intelligent VNF Profiling for Future Intelligent Network Orchestration

Cited by 2 publications

References 28 publications

iOn-Profiler: Intelligent Online Multi-Objective VNF Profiling With Reinforcement Learning

iOn-Profiler: Intelligent Online Multi-Objective VNF Profiling With Reinforcement Learning

Task Offloading Scheme for Survivability Guarantee Based on Traffic Prediction in 6G Edge Networks

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