{'fraction_non_alphanumeric': 0.019021739130434784, 'fraction_numerical': 0.010190217391304348, 'mean_word_length': 5.3218884120171674, '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}}
Scheduling Live Migration of Virtual Machines

Every day, numerous VMs are migrated inside a datacenter to balance the load, save energy or prepare production servers for maintenance. Although VM placement problems are carefully studied, the underlying migration schedulers rely on vague adhoc models. This leads to unnecessarily long and energy-intensive migrations. We present mVM, a new and extensible migration scheduler. To provide schedules with minimal completion times, mVM parallelizes and sequentializes the migrations with regards to the memory workload and the network topology. mVM is implemented as a plugin of BtrPlace and its current library allows administrators to address temporal and energy concerns. Experiments on a real testbed shows mVM outperforms state-of-the-art migration schedulers. Compared to schedulers that cap the migration parallelism, mVM reduces the individual migration duration by 20.4 percent on average and the schedule completion time by 28.1 percent. In a maintenance operation involving 96 VMs migrated between 72 servers, mVM saves 21.5 percent Joules against BtrPlace. Compared to the migration model inside the cloud simulator CloudSim, the prediction error of the migrations duration is about 5 times lower with mVM. By computing schedules involving thousands of migrations performed over various fat-tree network topologies, we observed that the mVM solving time accounts for about 1 percent of the schedule execution time.