{'fraction_non_alphanumeric': 0.01742627345844504, 'fraction_numerical': 0.0, 'mean_word_length': 5.72972972972973, '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}}
Runtime Resource Management with Workload Prediction

Modern embedded platforms need sophisticated resource managers in order to utilize the heterogeneous computational resources efficiently. Moreover, such platforms are exposed to fluctuating workloads unpredictable at design time. In such a context, predicting the incoming workload might improve the efficiency of resource management. But is this true? And, if yes, how significant is this improvement? How accurate does the prediction need to be in order to improve decisions instead of doing harm? By proposing a prediction-based resource manager aimed at minimizing energy consumption while meeting task deadlines and by running extensive experiments, we try to answer the above questions.