{'fraction_non_alphanumeric': 0.020599250936329586, 'fraction_numerical': 0.0046816479400749065, 'mean_word_length': 5.4397590361445785, '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}}
Thermal aware learning based CPU governor

The high temperature of a processor chip reduces its lifetime by many years. Also, higher temperature increases the cooling cost as well. Hence, controlling the peak temperature and maintaining high performance are the two important objectives that are in general trade‐off with each other. The dynamic voltage and frequency scaling is often used to reduce the energy consumption by running the processor on lower voltage and frequencies. This eventually decreases the temperature. In this article, we propose a linear regression‐based online CPU governor to predict the future temperature with high accuracy and later adjust the frequencies of the processor such that the temperature does not increase the given threshold. Doing so controls the peak temperature as well as maintains high performance. Extensive experimentation revealed that the proposed governor decreases the peak temperature by 4%–5% without observing any performance penalty and also reduces the execution cost by 3%–10% compared to the state‐of‐the‐art.