{'fraction_non_alphanumeric': 0.015544041450777202, 'fraction_numerical': 0.0, 'mean_word_length': 5.464435146443515, '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}}
Deadline constrained cloud computing resources scheduling for cost optimization based on dynamic objective genetic algorithm

Cloud computing resources scheduling is significant for executing the workflows in cloud platform because it relates to both the execution time and execution cost. In order to take both the time and cost into consideration, Rodriguez and Buyya have proposed a cost-minimization and deadline-constrained workflow scheduling model on cloud computing. Their model has great applicability but the solution of their particle swarm optimization (PSO) approach is not good enough and cannot meet a tight deadline condition. In this paper, we propose a genetic algorithm (GA) approach to solve this model. In order to tackle with the tight deadline condition, a dynamic objective strategy is further proposed to let GA focus on optimize the execution time objective to meet the deadline constraint when the feasible solution hasn't been obtained. After obtaining a feasible solution, the GA focuses on optimizing the execution cost within the deadline constraint. Therefore, the proposed dynamic objective GA (DOGA) has adaptive ability to the search environment to different objectives. We have conduct extensive experiments based on workflows with different scales and different cloud resources. Experimental results show that DOGA can find better solution with smaller cost than PSO does on different scheduling scales and different deadline conditions. DOGA approach is more applicable to be used in commercial activities.