{'fraction_non_alphanumeric': 0.010968921389396709, 'fraction_numerical': 0.0, 'mean_word_length': 5.84375, '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}}
Task Scheduling for Heterogeneous Computing Based on Bayesian Optimization Algorithm

Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platform, remains one of the challenge problems despite of numrous studies. This paper presents a novel scheduling algorithm based on Bayesian optimization algorithm (BOA) for heterogeneous computing environment. In the proposed algorithm, BOA constructs and updates Bayesian network according to the task graph of scheduling problems to find the optimal solution assigning tasks to different processors, and the execution sequence of tasks on the same processor is set by the heuristic used in the list scheduling approach. The proposed algorithm is sufficiently evaluated and compared with the related approaches by means of the empirical studies on benchmark applications. The experimental results confirm that the proposed algorithm is able to deliver more efficient schedules. Further experiments also indicate that the proposed algorithm maintains almost the same performance with different parameter settings.