LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data

• URL: http://arxiv.org/abs/2309.09479v1
• Date: Mon, 18 Sep 2023 04:27:05 GMT
• Title: LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data
• Abstract summary: We propose LogShrink, a novel and effective log compression method by leveraging commonality and variability of log data. An analyzer based on longest common subsequence and entropy techniques is proposed to identify the latent commonality and variability in log messages. The key idea behind this is that the commonality and variability can be exploited to shrink log data with a shorter representation.
• Score: 35.5712445690333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Log data is a crucial resource for recording system events and states during system execution. However, as systems grow in scale, log data generation has become increasingly explosive, leading to an expensive overhead on log storage, such as several petabytes per day in production. To address this issue, log compression has become a crucial task in reducing disk storage while allowing for further log analysis. Unfortunately, existing general-purpose and log-specific compression methods have been limited in their ability to utilize log data characteristics. To overcome these limitations, we conduct an empirical study and obtain three major observations on the characteristics of log data that can facilitate the log compression task. Based on these observations, we propose LogShrink, a novel and effective log compression method by leveraging commonality and variability of log data. An analyzer based on longest common subsequence and entropy techniques is proposed to identify the latent commonality and variability in log messages. The key idea behind this is that the commonality and variability can be exploited to shrink log data with a shorter representation. Besides, a clustering-based sequence sampler is introduced to accelerate the commonality and variability analyzer. The extensive experimental results demonstrate that LogShrink can exceed baselines in compression ratio by 16% to 356% on average while preserving a reasonable compression speed.

Related papers

• LogFold: Compressing Logs with Structured Tokens and Hybrid Encoding [36.603581308724465]
  We introduce LogFold, a novel log compression method consisting of four components.<n>A token analyzer classifies tokens as structured, unstructured, or static types; a processor mines recurring patterns within structured tokens based on their skeletons; a hybrid encoder tailors data representation according to token types.<n>Experiments on 16 public log datasets demonstrate that LogFold surpasses state-of-the-art baselines, achieving average compression ratio improvements by 11.11%, with a compression speed of 9.842 MB/s.
  arXiv  Detail & Related papers  (2026-03-21T03:27:37Z)
• DeLog: An Efficient Log Compression Framework with Pattern Signature Synthesis [30.690382753027137]
  A novel log compressor implements a Pattern Signature Synthesis mechanism to achieve efficient pattern-based grouping.<n>On 16 public and 10 production datasets, DeLog achieves state-of-the-art compression ratio and speed.
  arXiv  Detail & Related papers  (2026-01-21T15:26:09Z)
• LogLSHD: Fast Log Parsing with Locality-Sensitive Hashing and Dynamic Time Warping [2.415288727960745]
  Large-scale software systems generate vast volumes of system logs that are essential for monitoring, diagnosing, and performance optimization. LogLSHD demonstrates exceptional efficiency in parsing time, significantly outperforming state-of-the-art methods. For example, compared to Drain, LogLSHD reduces the average parsing time by 73% while increasing the average parsing accuracy by 15% on the LogHub 2.0 benchmark.
  arXiv  Detail & Related papers  (2025-04-02T23:08:04Z)
• Log2graphs: An Unsupervised Framework for Log Anomaly Detection with Efficient Feature Extraction [1.474723404975345]
  High cost of manual annotation and dynamic nature of usage scenarios present major challenges to effective log analysis. This study proposes a novel log feature extraction model called DualGCN-LogAE, designed to adapt to various scenarios. We also introduce Log2graphs, an unsupervised log anomaly detection method based on the feature extractor.
  arXiv  Detail & Related papers  (2024-09-18T11:35:58Z)
• LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection [73.69399219776315]
  We propose a unified Transformer-based framework for Log anomaly detection (LogFormer) to improve the generalization ability across different domains. Specifically, our model is first pre-trained on the source domain to obtain shared semantic knowledge of log data. Then, we transfer such knowledge to the target domain via shared parameters.
  arXiv  Detail & Related papers  (2024-01-09T12:55:21Z)
• GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection [49.9884374409624]
  GLAD is a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs. We introduce GLAD, a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs.
  arXiv  Detail & Related papers  (2023-09-12T04:21:30Z)
• A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We? [42.56249610409624]
  We provide a new collection of annotated log datasets, denoted Loghub-2.0, which can better reflect the characteristics of log data in real-world software systems. We conduct a thorough re-evaluation of 15 state-of-the-art logs in a more rigorous and practical setting. Particularly, we introduce a new evaluation metric to mitigate the sensitivity of existing metrics to imbalanced data distributions.
  arXiv  Detail & Related papers  (2023-08-21T16:24:15Z)
• Log Parsing Evaluation in the Era of Modern Software Systems [47.370291246632114]
  We focus on one integral part of automated log analysis, log parsing, which is the prerequisite to deriving any insights from logs. Our investigation reveals problematic aspects within the log parsing field, particularly its inefficiency in handling heterogeneous real-world logs. We propose a tool, Logchimera, that enables estimating log parsing performance in industry contexts.
  arXiv  Detail & Related papers  (2023-08-17T14:19:22Z)
• ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection [3.3196401064045014]
  This study proposes ClusterLog, a log pre-processing method that clusters the temporal sequence of log keys based on their semantic similarity. By grouping semantically and sentimentally similar logs, this approach aims to represent log sequences with the smallest amount of unique log keys, intending to improve the ability of a downstream sequence-based model to effectively learn the log patterns.
  arXiv  Detail & Related papers  (2023-01-19T01:54:48Z)
• LogGD:Detecting Anomalies from System Logs by Graph Neural Networks [14.813971618949068]
  We propose a novel graph-based log anomaly detection method, LogGD, to effectively address the issue. We exploit the powerful capability of Graph Transformer Neural Network, which combines graph structure and node semantics for log-based anomaly detection.
  arXiv  Detail & Related papers  (2022-09-16T11:51:58Z)
• Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models [59.04636530383049]
  Anomalies or failures in large computer systems, such as the cloud, have an impact on a large number of users. We propose a framework for anomaly detection in log data, as a major troubleshooting source of system information.
  arXiv  Detail & Related papers  (2021-02-23T09:17:05Z)
• Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs [59.04636530383049]
  We propose Logsy, a classification-based method to learn log representations. We show an average improvement of 0.25 in the F1 score, compared to the previous methods.
  arXiv  Detail & Related papers  (2020-08-21T07:26:55Z)

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.