Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight

• URL: http://arxiv.org/abs/2407.08694v1
• Date: Thu, 11 Jul 2024 17:31:12 GMT
• Title: Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight
• Authors: Zhiqiang Xie, Yujia Zheng, Lizi Ottens, Kun Zhang, Christos Kozyrakis, Jonathan Mace,
• Abstract summary: We present Atlas, a novel approach to automatically synthesizing causal graphs for cloud systems. We evaluate Atlas across a range of fault localization scenarios and demonstrate that Atlas is capable of generating causal graphs in a scalable and generalizable manner.
• Score: 12.272468397322738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Runtime failure and performance degradation is commonplace in modern cloud systems. For cloud providers, automatically determining the root cause of incidents is paramount to ensuring high reliability and availability as prompt fault localization can enable faster diagnosis and triage for timely resolution. A compelling solution explored in recent work is causal reasoning using causal graphs to capture relationships between varied cloud system performance metrics. To be effective, however, systems developers must correctly define the causal graph of their system, which is a time-consuming, brittle, and challenging task that increases in difficulty for large and dynamic systems and requires domain expertise. Alternatively, automated data-driven approaches have limited efficacy for cloud systems due to the inherent rarity of incidents. In this work, we present Atlas, a novel approach to automatically synthesizing causal graphs for cloud systems. Atlas leverages large language models (LLMs) to generate causal graphs using system documentation, telemetry, and deployment feedback. Atlas is complementary to data-driven causal discovery techniques, and we further enhance Atlas with a data-driven validation step. We evaluate Atlas across a range of fault localization scenarios and demonstrate that Atlas is capable of generating causal graphs in a scalable and generalizable manner, with performance that far surpasses that of data-driven algorithms and is commensurate to the ground-truth baseline.

Related papers

• Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset [1.293050392312921]
  We introduce a new high-dimensional dataset from IBM Cloud, collected over 4.5 months from the IBM Cloud Console. This dataset comprises 39,365 rows and 117,448 columns of telemetry data. We demonstrate the application of machine learning models for anomaly detection and discuss the key challenges faced in this process.
  arXiv  Detail & Related papers  (2024-11-13T22:04:19Z)
• 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)
• Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image [24.09239785062109]
  We develop a novel dataset for accurate cloud recognition. We use domain adaptation methods to align 70,419 image-label pairs in terms of projection, temporal resolution, and spatial resolution. We also introduce a Distribution-aware Interactive-Attention Network (DIAnet), which preserves pixel-level details through a high-resolution branch and a parallel cross-branch.
  arXiv  Detail & Related papers  (2024-01-06T09:58:09Z)
• Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features [11.83269525626691]
  Cloud systems are susceptible to performance issues, which may cause service-level agreement violations and financial losses. We propose a learning-based approach that leverages both the relational and temporal features of metrics to identify performance issues.
  arXiv  Detail & Related papers  (2023-07-20T13:41:26Z)
• Deep Temporal Graph Clustering [77.02070768950145]
  We propose a general framework for deep Temporal Graph Clustering (GC) GC introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. Our framework can effectively improve the performance of existing temporal graph learning methods.
  arXiv  Detail & Related papers  (2023-05-18T06:17:50Z)
• Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data [44.64190826937705]
  We present scalable algorithms for detecting label errors and outlier data based on the relational graph structure of data. We also introduce a visualization tool that provides contextual information of a data point in the feature-embedded space. Our approach achieves state-of-the-art detection performance on all tasks considered and demonstrates its effectiveness in large-scale real-world datasets.
  arXiv  Detail & Related papers  (2023-01-29T02:09:13Z)
• Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning [0.11470070927586014]
  This paper presents a novel approach for analyzing failure data from cloud systems, in order to relieve human analysts from manually fine-tuning the data for feature engineering. The approach leverages Deep Embedded Clustering (DEC), a family of unsupervised clustering algorithms based on deep learning. The results show that the performance of the proposed approach, in terms of purity of clusters, is comparable to, or in some cases even better than manually fine-tuned clustering.
  arXiv  Detail & Related papers  (2021-06-29T09:00:41Z)
• Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies [58.88325379746632]
  We present Arvalus and its variant D-Arvalus, a neural graph transformation method that models system components as nodes and their dependencies as edges to improve the identification and localization of anomalies. Given a series of metric, our method predicts the most likely system state - either normal or an anomaly class - and performs localization when an anomaly is detected. The evaluation shows the generally good prediction performance of Arvalus and reveals the advantage of D-Arvalus which incorporates information about system component dependencies.
  arXiv  Detail & Related papers  (2021-03-09T06:34:05Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• Adaptive Graph Auto-Encoder for General Data Clustering [90.8576971748142]
  Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. We propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs.
  arXiv  Detail & Related papers  (2020-02-20T10:11:28Z)

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.