{'fraction_non_alphanumeric': 0.020338983050847456, 'fraction_numerical': 0.0, 'mean_word_length': 5.661654135338346, '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}}
Evaluation of topology optimization objectives

Two network-wide optimization contexts are traffic engineering and topology optimization. Various optimization objective functions and metrics have been proposed for both contexts. Yet, it is hard to evaluate the efficiency of those optimization objectives. Previously, a study analyzed the efficiency of some optimization metrics for traffic engineering by using linear programming (LP). On the other hand, in the topology optimization domain, there has not been any work on evaluation of different metrics. Because, it is hard to evaluate these metrics as the optimization algorithms are objective function tailored heuristics generally. As a result, a fair comparison of different objectives becomes hard. In this work, using machine learning we compare and analyze different traffic optimization objectives for topology optimization.