{'fraction_non_alphanumeric': 0.01809954751131222, 'fraction_numerical': 0.027149321266968326, 'mean_word_length': 5.4678362573099415, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Characterizing X86 and ARM Serverless Performance Variation: A Natural Language Processing Case Study

In this paper, we leverage a Natural Language Processing (NLP) pipeline for topic modeling consisting of three functions for data preprocessing, model training, and inferencing to analyze serverless platform performance variation. Specifically, we investigated performance using x86_64 and ARM64 processors over a 24-hour day starting at midnight local time on four cloud regions across three continents on AWS Lambda. We identified public cloud resource contention by leveraging the CPU steal metric, and examined relationships to NLP pipeline runtime. Intel x86_64 Xeon processors at the same clock rate as ARM64 processors (Graviton 2) were more than 23% faster for model training, but ARM64 processors were faster for data preprocessing and inferencing. Use of the Intel x86_64 architecture for the NLP pipeline was up to 33.4% more expensive than ARM64 as a result of incentivized pricing from the cloud provider and slower pipeline runtime due to greater resource contention for Intel processors.