Machine Learning for Performance-Aware Virtual Network Function Placement

• URL: http://arxiv.org/abs/2001.07787v1
• Date: Mon, 13 Jan 2020 14:08:39 GMT
• Title: Machine Learning for Performance-Aware Virtual Network Function Placement
• Authors: Dimitrios Michael Manias, Manar Jammal, Hassan Hawilo, Abdallah Shami, Parisa Heidari, Adel Larabi, Richard Brunner
• Abstract summary: We develop a machine learning decision tree model that learns from the effective placement of the various Virtual Network Function instances forming a Service Function Chain (SFC) The model takes several performance-related features from the network as an input and selects the placement of the various VNF instances on network servers with the objective of minimizing the delay between dependent VNF instances.
• Score: 3.5558885788605323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased connectivity demand. Although Network Function Virtualization (NFV) has been identified as a solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) placement problem by developing a machine learning decision tree model that learns from the effective placement of the various VNF instances forming a Service Function Chain (SFC). The model takes several performance-related features from the network as an input and selects the placement of the various VNF instances on network servers with the objective of minimizing the delay between dependent VNF instances. The benefits of using machine learning are realized by moving away from a complex mathematical modelling of the system and towards a data-based understanding of the system. Using the Evolved Packet Core (EPC) as a use case, we evaluate our model on different data center networks and compare it to the BACON algorithm in terms of the delay between interconnected components and the total delay across the SFC. Furthermore, a time complexity analysis is performed to show the effectiveness of the model in NFV applications.

Related papers

• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers. This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Time-sensitive Learning for Heterogeneous Federated Edge Intelligence [52.83633954857744]
  We investigate real-time machine learning in a federated edge intelligence (FEI) system. FEI systems exhibit heterogenous communication and computational resource distribution. We propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model.
  arXiv  Detail & Related papers  (2023-01-26T08:13:22Z)
• Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains [0.0]
  anomaly detection system (ADS) for virtual network functions in service function chains (SFCs) We propose several sequential deep learning models to learn time-series patterns and sequential patterns of the virtual network functions (VNFs) in the chain with variable lengths.
  arXiv  Detail & Related papers  (2021-09-29T08:47:57Z)
• Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning [9.780232937571599]
  Deep reinforcement learning (DRL) has appeared as a viable way to solve such problems. In this paper, we first utilize single agent low-complex compound action actor-critic RL to cover both discrete and continuous actions. We then extend our solution to a multi-agent DRL scheme in which agents collaborate with each other.
  arXiv  Detail & Related papers  (2021-05-10T09:04:49Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach [0.0]
  We propose a model-free deep reinforcement learning (DRL)-based proactive failure recovery framework called zero-touch PFR (ZT-PFR) ZT-PFR is for the embedded stateful virtual network functions (VNFs) in network function virtualization (NFV) enabled networks.
  arXiv  Detail & Related papers  (2021-02-02T21:40:35Z)
• A Machine Learning-Based Migration Strategy for Virtual Network Function Instances [3.7783523378336104]
  We develop the VNF Neural Network for Instance Migration (VNNIM), a migration strategy for VNF instances. VNNIM is very effective in predicting the post-migration server exhibiting a binary accuracy of 99.07%. The greatest advantage of VNNIM, however, is its run-time efficiency highlighted through a run-time analysis.
  arXiv  Detail & Related papers  (2020-06-15T15:03:27Z)
• Depth-Optimized Delay-Aware Tree (DO-DAT) for Virtual Network Function Placement [3.5584529568201377]
  Network Function (NFV) has been identified as a solution, but several challenges must be addressed to ensure its feasibility. We present a machine learning-based solution to the Virtual Network (VNF) placement problem.
  arXiv  Detail & Related papers  (2020-06-02T17:18:20Z)
• Toward fast and accurate human pose estimation via soft-gated skip connections [97.06882200076096]
  This paper is on highly accurate and highly efficient human pose estimation. We re-analyze this design choice in the context of improving both the accuracy and the efficiency over the state-of-the-art. Our model achieves state-of-the-art results on the MPII and LSP datasets.
  arXiv  Detail & Related papers  (2020-02-25T18:51:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.