{'fraction_non_alphanumeric': 0.018354430379746836, 'fraction_numerical': 0.0, 'mean_word_length': 5.6991525423728815, '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}}
An Optimization Framework of Adaptive Computing-plus-Communication for Multimedia Processing in Cloud: A Review

Abstract: Clear trend within the evolution of network-based services is that the ever-increasing amount of multimedia system data concerned. This trend towards big-data multimedia system process finds its natural placement at the side of the adoption of the cloud computing paradigm, that looks the most effective solution to the strain of a extremely fluctuating work that characterizes this sort of services. However, as cloud data centers become a lot of and a lot of powerful, energy consumption becomes a significant challenge each for environmental concerns and for economic reasons. An effective approach to improve energy efficiency in cloud data centers is to rely on traffic engineering techniques to dynamically adapt the number of active servers to the current workload. Towards this aim, we propose a joint computing-plus-communication improvement framework exploiting virtualization technologies. Our proposal specifically addresses the everyday situation of data processing processing with computationally intensive tasks and exchange of a giant volume of data. The proposed framework not only ensures users the Quality of Service, but also achieves maximum energy saving and attains green cloud computing goals in a fully distributed fashion by utilizing the DVFS-based CPU frequencies Keywords: Energy efficiency, Multimedia data processing, Cloud resource management, Load balancing, Dynamic voltage and frequency scaling (DVFS), Traffic engineering