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Logs have been widely adopted in software system development and maintenancebecause of the rich runtime information they record. In recent years, theincrease of software size and complexity leads to the rapid growth of thevolume of logs. To handle these large volumes of logs efficiently andeffectively, a line of research focuses on developing intelligent and automatedlog analysis techniques. However, only a few of these techniques have reachedsuccessful deployments in industry due to the lack of public log datasets andopen benchmarking upon them. To fill this significant gap and facilitate moreresearch on AI-driven log analytics, we have collected and released loghub, alarge collection of system log datasets. In particular, loghub provides 19real-world log datasets collected from a wide range of software systems,including distributed systems, supercomputers, operating systems, mobilesystems, server applications, and standalone software. In this paper, wesummarize the statistics of these datasets, introduce some practical usagescenarios of the loghub datasets, and present our benchmarking results onloghub to benefit the researchers and practitioners in this field. Up to thetime of this paper writing, the loghub datasets have been downloaded forroughly 90,000 times in total by hundreds of organizations from both industryand academia. The loghub datasets are available athttps://github.com/logpai/loghub.

Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics