{'fraction_non_alphanumeric': 0.01684836471754212, 'fraction_numerical': 0.0009910802775024777, 'mean_word_length': 4.976331360946745, '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}}
Interarrival time distribution for a non-Markovian arrival process

We consider a probabilistic model for workload input into a telecommunication system. It captures the dynamics of packet generation in data traffic as well as accounting for long-range dependence and self-similarity exhibited by real traces. The workload has stationary increments as observed in high-speed data networks for certain periods. Such periods with peak demand can be studied with this model for further queuing performance analysis. The workload is found by aggregating the number of packets, or their sizes, generated by the arriving sessions. The arrival time, the duration and the packet generation process of a session are all governed by a Poisson random measure. The model has been recently used in a G/M/1 queuing system with priority where the interarrival time distribution has been approximated. We show that it is a good approximation due to good match of the computed performance measures with the simulation results.