{'fraction_non_alphanumeric': 0.0267639902676399, 'fraction_numerical': 0.0016220600162206002, 'mean_word_length': 4.961352657004831, '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}}
Intelligent Mitigation in Multilevel Feedback Queues

The performance of Multi-Level Feedback Queues (MLFQ) has been explored as a mechanism for allocating CPU time in a multiprogramming operating systems. MLFQ-based systems have the advantage of not requiring data to be saved and updated for each process after each burst of CPU time. Thus, the overhead computation time to run the scheduling algorithm is small. But MLFQs along with other algorithms risk starvation of those processes needing large CPU bursts, which drop down to the lowest-priority queue(N) of the stack of queues. This research extends previous work investigating the safety of reallocating a small quantity of CPU time from higher-priority queues to the final queue in order to prevent starvation called MLFQ-No Starvation (MLFQ-NS). This research explores an extension of MLFQ-NS with the new idea of Intelligent Mitigation (MLFQ-IM) which redirects time not just to the final queue Qn, but also to Qn-1, which is studied through simulation to understand its effectiveness and safety in reallocating a percentage of CPU time to the two lowest priority queues Queue(N) and Queue(N-1) when under heavy load, at and exceeding the maximum CPU processing capacity.