Machine learning helps, but more data catches more faults - so Chinese champ has shared its data
Alibaba Cloud has revealed homebrew tech it used to improve server fault prediction and detection, which it claims saw its ability to detect problems beat comparable tech by ten percent.The document points out that reliability is a major selling point for public clouds, making predicting failures an important ability. Log files, the authors observe, contain plenty of info on"exceptions" to normal performance that indicate potential performance problems.
Alibaba Cloud therefore created its own tool called Time-Aware Attention-Based Transformer to analyze timestamp info. TAAT doesn't entirely ignore ML tools. Instead, it uses the Bidirectional Encoder Representations from Transformers – a language model developed by Google that represents text as vectors and has been used to predict server failures. The paper asserts, however, that BERT hasn't been tuned to make full use of log timestamps.
Alibaba's tool therefore relies on BERT for some failure analysis and compares that with TAAT's analysis of logfile timestamps. The paper contains a lot of math describing exactly how Alibaba analyzes log info, but the bottom line was apparently a ten percent improvement in fault predictions – and presumably slightly more reliable cloudy IaaS.
Alibaba's boffins think TAAT's output is also useful because it doesn't need expert analysis – meaning folks familiar with cloudy crashes aren't needed to help as often. It's already in production at Alibaba Cloud.a colossal dataset comprising"∼2.
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