{'fraction_non_alphanumeric': 0.012804097311139564, 'fraction_numerical': 0.0, 'mean_word_length': 5.539748953974895, '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}}
Optimized PSO-EFA Algorithm for Energy Efficient Virtual Machine Migrations

The expansion of cloud infrastructure follows with increase in number of data centers hosting number of computing nodes and then, it becomes the reason for huge amount of energy consumption across the world. However, benefits of cloud computing industry with its low-price and high productivity keep diverting the attention of organizations from environmental mess and high energy cost incurred by the data centers. Therefore, it becomes very urgent to curtail the increase in requirement of energy for cloud service providers with the provision of sufficient quality of service to end users. The best way to achieve the balance between energy usage and quality of service is workload aware energy efficient Virtual Machine (VM) consolidation. The various parameters are managed to strike the trade-off between energy consumption and cloud services. This paper presents the optimized PSO-EFA algorithm for energy efficiency with workload management in terms of number of migrations and number of systems shut down during migration process of consolidation. This study paved the way forward for energy efficient cloud environment during migration process. The simulation conducted in constrained environment indicated that workload variation has significant impact on different energy consumption allied parameters. The PSO-EFA algorithm outperformed existing base algorithm for energy consumption and other parameters. The proposed algorithm worked in sync with sustainability efforts.