Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network

• URL: http://arxiv.org/abs/2410.17762v1
• Date: Wed, 23 Oct 2024 11:01:39 GMT
• Title: Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network
• Authors: Suraj Kumar, Soumi Chattopadhyay, Chandranath Adak,
• Abstract summary: Quality-of-Service (QoS) prediction is a critical task in the service lifecycle. Traditional methods often encounter data sparsity and cold-start issues. We introduce a real-time, trust-aware framework for temporal prediction.
• Score: 0.47248250311484113
• License:
• Abstract: Quality-of-Service (QoS) prediction is a critical task in the service lifecycle, enabling precise and adaptive service recommendations by anticipating performance variations over time in response to evolving network uncertainties and user preferences. However, contemporary QoS prediction methods frequently encounter data sparsity and cold-start issues, which hinder accurate QoS predictions and limit the ability to capture diverse user preferences. Additionally, these methods often assume QoS data reliability, neglecting potential credibility issues such as outliers and the presence of greysheep users and services with atypical invocation patterns. Furthermore, traditional approaches fail to leverage diverse features, including domain-specific knowledge and complex higher-order patterns, essential for accurate QoS predictions. In this paper, we introduce a real-time, trust-aware framework for temporal QoS prediction to address the aforementioned challenges, featuring an end-to-end deep architecture called the Hypergraph Convoluted Transformer Network (HCTN). HCTN combines a hypergraph structure with graph convolution over hyper-edges to effectively address high-sparsity issues by capturing complex, high-order correlations. Complementing this, the transformer network utilizes multi-head attention along with parallel 1D convolutional layers and fully connected dense blocks to capture both fine-grained and coarse-grained dynamic patterns. Additionally, our approach includes a sparsity-resilient solution for detecting greysheep users and services, incorporating their unique characteristics to improve prediction accuracy. Trained with a robust loss function resistant to outliers, HCTN demonstrated state-of-the-art performance on the large-scale WSDREAM-2 datasets for response time and throughput.

Related papers

• Scalable and Effective Negative Sample Generation for Hyperedge Prediction [55.9298019975967]
  Hyperedge prediction is crucial for understanding complex multi-entity interactions in web-based applications. Traditional methods often face difficulties in generating high-quality negative samples due to imbalance between positive and negative instances. We present the scalable and effective negative sample generation for Hyperedge Prediction (SEHP) framework, which utilizes diffusion models to tackle these challenges.
  arXiv  Detail & Related papers  (2024-11-19T09:16:25Z)
• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a generative Transformer for unified time series forecasting. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• GACL: Graph Attention Collaborative Learning for Temporal QoS Prediction [5.040979636805073]
  We propose a novel Graph Collaborative Learning (GACL) framework for temporal prediction. It builds on a dynamic user-service graph to comprehensively model historical interactions. Experiments on the WS-DREAM dataset demonstrate that GACL significantly outperforms state-of-the-art methods for temporal prediction.
  arXiv  Detail & Related papers  (2024-08-20T05:38:47Z)
• Large Language Model Meets Graph Neural Network in Knowledge Distillation [7.686812700685084]
  We propose a temporal-aware framework for predicting Quality of Service (QoS) in service-oriented architectures. Our proposed TOGCL framework significantly outperforms state-of-the-art methods across multiple metrics, achieving improvements of up to 38.80%.
  arXiv  Detail & Related papers  (2024-02-08T18:33:21Z)
• ARRQP: Anomaly Resilient Real-time QoS Prediction Framework with Graph Convolution [0.16317061277456998]
  We introduce a real-time prediction framework (called ARRQP) with a specific emphasis on improving resilience to anomalies in the data. ARRQP integrates both contextual information and collaborative insights, enabling a comprehensive understanding of user-service interactions. Results on the benchmark WS-DREAM dataset demonstrate the framework's effectiveness in achieving accurate and timely predictions.
  arXiv  Detail & Related papers  (2023-09-22T04:37:51Z)
• CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting [50.23240107430597]
  We design a special Transformer, i.e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting. First, CARD introduces a channel-aligned attention structure that allows it to capture both temporal correlations among signals. Second, in order to efficiently utilize the multi-scale knowledge, we design a token blend module to generate tokens with different resolutions. Third, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue.
  arXiv  Detail & Related papers  (2023-05-20T05:16:31Z)
• TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features [0.5161531917413706]
  Temporal Prediction is essential to identify a suitable service over time. Recent methods hardly achieved desired accuracy due to various limitations. This paper proposes a scalable strategy for Temporal Prediction using Multi-source Collaborative-Features.
  arXiv  Detail & Related papers  (2023-03-30T06:49:53Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications [0.0]
  Predictive uncertainty supplements model predictions and enables improved functionality of downstream tasks. We tackle this problem by building upon Monte Carlo Dropout (MCDO) models using the Axolotl framework. We conduct experiments on (1) a multi-class classification task using the CIFAR10 dataset, and (2) a more complex human body segmentation task.
  arXiv  Detail & Related papers  (2021-11-11T22:24:15Z)
• Bilinear Input Normalization for Neural Networks in Financial Forecasting [101.89872650510074]
  We propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme takes into account the bimodal characteristic of financial time-series. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data, show significant improvements over other normalization techniques.
  arXiv  Detail & Related papers  (2021-09-01T07:52:03Z)

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.