{'fraction_non_alphanumeric': 0.023693379790940768, 'fraction_numerical': 0.005574912891986063, 'mean_word_length': 5.243478260869566, '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}}
A Research on an Efficient Cloud Scheduling with a Geo Microarray Data Set

Investigations on micro-array organisms for various researches have made a non discrete dealing of thousands of gene expressions achievable. For any applications, the results would be more accurate only when maximum count is analyzed within a predictable time and it is one of the unseen challenges in the field of bio medicine. The purpose of this data analysis is to regulate and control the activities of thousands of genes in our body. This paper develops a scheduling analysis of how effectively gene molecular patterns are taken into experimentation. This motivated our investigation in a new dimension for a cloud environment. This paper is about applying our previous works such as Workflow Shuffling and Hole Filling Algorithm (WSHF) [13], Agent Centric Enhanced Reinforcement learning algorithm (AGERL) [14], Heuristic Flow Equilibrium based Load Balancing (HFEL) [15] and Dynamic Resource Provisioning and Load Balancing (DRBLHS) [16] algorithms collaboratively for a Gene Express Omnibus dataset as a case study. The gene data’s plays an important role in monitoring the human activities and how well, the data has been processed in the cloud with minimum budget, time and minimum virtual machines. Finally, the efficiency of the system is analyzed in terms of resource utilization, completion time, response time, throughput and VM Migration time