{'fraction_non_alphanumeric': 0.019627085377821395, 'fraction_numerical': 0.001962708537782139, 'mean_word_length': 5.496815286624204, '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}}
Adaptive Differentiated Edge Caching with Machine Learning for V2X Communication

Connected vehicles that communicate with the traffic network around them have several uses in providing road safety and infotainment. Such applications leverage on Vehicle-to-Anything (V2X) communication, which is challenging because of rapidly changing topology and traffic patterns. We propose a differentiated edge caching scheme called FlexiCache for such networks. In FlexiCache, the cache is split into sections to hold data of different classes with suitable replacement policies. Further, FlexiCache uses kernel ridge regression (KRR) to predict the proportion of cache to be allocated to each traffic type, for a desired quality of service(QoS) parameter. It then uses a self-learning mechanism that adapts cache allocation to the network conditions. Simulation results show that FlexiCache performs better than undifferentiated caching and also that the predictions by KRR result in QoS which is very close to the target value.