{'fraction_non_alphanumeric': 0.015680684248039915, 'fraction_numerical': 0.0014255167498218105, 'mean_word_length': 5.591549295774648, '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}}
Game theory based dynamic resource allocation for hybrid environment with cloud and big data application

Virtualization based cloud and big data applications have been widely adopted in various fields. Because deploying the big data applications on the cloud will cause obvious performance degradation, the cloud and big data applications are provided with fixed resource separately. However, the traditional fixed resource allocation mechanism has two drawbacks: (1) low resource utility and (2) unresponsiveness to the performance degradation. To address these drawbacks, the cloud and big data hybrid environment is designed, where fair resource allocation is used to ensure fairness between cloud and big data applications while virtual machine migration is used to make each virtual machine in cloud application reach its own satisfactory. Herein, game theory is used to model the conflict and negotiation between cloud and big data applications. Firstly, the Nash Equilibrium is used to discover the best strategy for both applications. Secondly, as for virtual machine migration, we use Nash Bargaining game to present the situation where virtual machines compete for more resources allocation while their minimal demand is ensured. Finally, experiments are carried out to prove that the hybrid environment outperforms the traditional method both in resource utility and application performance.