{'fraction_non_alphanumeric': 0.021111111111111112, 'fraction_numerical': 0.0, 'mean_word_length': 5.576642335766423, '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}}
Towards a client-side QoS monitoring and assessment using Generalized Pareto Distribution in a cloud-based environment

Recently, cloud computing and cloud-based services have been gaining a lot of momentum due to their cost effectiveness. Assuring Quality-of-Service (QoS) guarantees to the cloud's mobile clients is an important and unavoidable problem especially for delay sensitive application like VoIP. In this paper, we propose a novel approach for the early detection of cloud service degradations. The proposed algorithms employ the Generalized Pareto Distribution (GPD) approach to converge to a precise QoS model based on collected delay measurements from the cloud's mobile clients. Our experimental results demonstrate the usefulness of the proposed techniques and their ability to perform early detection of performance degradation of the cloud's infrastructure and its hosted services.