Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems

• URL: http://arxiv.org/abs/2408.08968v4
• Date: Fri, 11 Apr 2025 16:19:31 GMT
• Title: Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems
• Authors: Cyril Shih-Huan Hsu, Danny De Vleeschauwer, Chrysa Papagianni, Paola Grosso,
• Abstract summary: This study investigates the dynamic nature of real-world systems and introduces an online learning-decomposition framework to tackle the dynamicity.<n>We propose a framework that continuously updates the risk models based on the most recent feedback.<n>Our empirical study on an analytic model-based simulator demonstrates that the proposed framework outperforms the state-of-the-art static approach.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When a network slice spans multiple technology domains, it is crucial for each domain to uphold the End-to-End (E2E) Service Level Agreement (SLA) associated with the slice. Consequently, the E2E SLA must be properly decomposed into partial SLAs that are assigned to each domain involved. In a network slice management system with a two-level architecture, comprising an E2E service orchestrator and local domain controllers, we consider that the orchestrator has access only to historical data regarding the responses of local controllers to previous requests, and this information is used to construct a risk model for each domain. In this study, we extend our previous work by investigating the dynamic nature of real-world systems and introducing an online learning-decomposition framework to tackle the dynamicity. We propose a framework that continuously updates the risk models based on the most recent feedback. This approach leverages key components such as online gradient descent and FIFO memory buffers, which enhance the stability and robustness of the overall process. Our empirical study on an analytic model-based simulator demonstrates that the proposed framework outperforms the state-of-the-art static approach, delivering more accurate and resilient SLA decomposition under varying conditions and data limitations. Furthermore, we provide a comprehensive complexity analysis of the proposed solution.

Related papers

• Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization [68.41367635546183]
  Single Domain Generalization aims to train models with consistent performance across diverse scenarios using data from a single source. We propose Discriminative Domain Reassembly and Soft-Fusion (DRSF), a training framework leveraging synthetic data to improve model generalization.
  arXiv  Detail & Related papers  (2025-03-17T18:08:03Z)
• RAILS: Risk-Aware Iterated Local Search for Joint SLA Decomposition and Service Provider Management in Multi-Domain Networks [0.0]
  5G technology has transformed mobile networks into multi-service environments, necessitating efficient network slicing to meet diverse Service Level Agreements (SLAs) This paper introduces Risk-Aware Iterated Local Search (RAILS), a novel risk model-driven meta-heuristic framework designed to jointly address SLA decomposition and service provider selection in multi-domain networks.
  arXiv  Detail & Related papers  (2025-02-10T17:00:32Z)
• A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations [2.7755345520127936]
  We propose a domain-decomposition-based deep learning (DL) framework, named CoMLSim, for accurately modeling unsteady and nonlinear partial differential equations (PDEs) The framework consists of two key components: (a) a convolutional neural network (CNN)-based autoencoder architecture and (b) an autoregressive model composed of fully connected layers.
  arXiv  Detail & Related papers  (2024-08-26T17:50:47Z)
• StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization [85.18995948334592]
  Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data. We propose emphStyDeSty, which explicitly accounts for the alignment of the source and pseudo domains in the process of data augmentation.
  arXiv  Detail & Related papers  (2024-06-01T02:41:34Z)
• Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing [46.65030115953947]
  We propose a framework consisting of a digital twin and reinforcement learning agents. Specifically, we propose to use the historical data and the neural networks to build a digital twin model to simulate the state variation law of the real environment. We also extend the framework to offline reinforcement learning, where solutions can be used to obtain intelligent decisions based solely on historical data.
  arXiv  Detail & Related papers  (2023-11-28T15:25:14Z)
• Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
  We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation. We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
  arXiv  Detail & Related papers  (2023-11-03T13:34:44Z)
• Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning [71.02384943570372]
  Family Offline-to-Online RL (FamO2O) is a framework that empowers existing algorithms to determine state-adaptive improvement-constraint balances. FamO2O offers a statistically significant improvement over various existing methods, achieving state-of-the-art performance on the D4RL benchmark.
  arXiv  Detail & Related papers  (2023-10-27T08:30:54Z)
• Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks [44.37047471448793]
  In this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL) We propose an innovative PSL framework, namely, efficient parallel split learning (EPSL) to accelerate model training. We show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy.
  arXiv  Detail & Related papers  (2023-03-26T16:09:48Z)
• Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites [52.84959869494459]
  In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites. Existing methods are usually restricted in either network memorizability on previous sites or generalizability on unseen sites. This paper aims to tackle the problem of Synchronous Memorizability and Generalizability with a novel proposed SMG-learning framework.
  arXiv  Detail & Related papers  (2022-06-14T13:04:36Z)
• Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI [29.11998008894847]
  We propose a data-driven deep learning-based unified hybrid beamforming framework for time division duplex and frequency division duplex systems. For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network.
  arXiv  Detail & Related papers  (2022-01-18T07:21:00Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning [11.420934703203878]
  We propose OnSlicing, an online end-to-end network slicing system, to achieve minimal resource usage while satisfying slices' SLA. OnSlicing allows individualized learning for each slice and maintains its SLA by using a novel constraint-aware policy update method. We implement OnSlicing on an end-to-end slicing testbed designed based on OpenAirInterface with both 4G LTE and 5G NR, OpenDayLight SDN platform, and OpenAir-CN core network.
  arXiv  Detail & Related papers  (2021-11-02T14:25:58Z)
• Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis [96.53859361560505]
  Aspect-based Sentiment Analysis (ABSA) aims to determine the sentiment polarity towards an aspect. There always exists severe domain shift between the pretraining and downstream ABSA datasets. We introduce a unified alignment pretraining framework into the vanilla pretrain-finetune pipeline.
  arXiv  Detail & Related papers  (2021-10-26T04:03:45Z)
• Source-Free Open Compound Domain Adaptation in Semantic Segmentation [99.82890571842603]
  In SF-OCDA, only the source pre-trained model and the target data are available to learn the target model. We propose the Cross-Patch Style Swap (CPSS) to diversify samples with various patch styles in the feature-level. Our method produces state-of-the-art results on the C-Driving dataset.
  arXiv  Detail & Related papers  (2021-06-07T08:38:41Z)
• Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction [94.25865526414717]
  This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos. We propose Bilevel Online Adaptation, which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks.
  arXiv  Detail & Related papers  (2021-03-30T15:47:58Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Generalized Variational Continual Learning [33.194866396158005]
  Two main approaches to continuous learning are Online Elastic Weight Consolidation and Variational Continual Learning. We show that applying this modification to mitigate Online EWC as a limiting case, allowing baselines between the two approaches. In order to the observed overpruning effect of VI, we take inspiration from a common multi-task architecture, mitigate neural networks with task-specific FiLM layers.
  arXiv  Detail & Related papers  (2020-11-24T19:07:39Z)
• ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation [111.56730703473411]
  Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations. Simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels. ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning.
  arXiv  Detail & Related papers  (2020-09-07T23:46:08Z)
• Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment [28.61782696432711]
  We propose a novel Unsupervised Domain Adaptation (UDA) strategy to address the domain shift issue between real world and synthetic representations. We show how the proposed strategy is able to obtain impressive performance in adapting a segmentation network trained on synthetic data to real world scenarios.
  arXiv  Detail & Related papers  (2020-01-14T10:12:20Z)

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.