{'fraction_non_alphanumeric': 0.01808066759388039, 'fraction_numerical': 0.0, 'mean_word_length': 5.260869565217392, '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}}
LESG: Learning and economic based scheduler implementation

In a dynamic environment like grid with huge number of subtasks, flexible approach is necessary to manage resource allocation. Grid is a robust technique in parallel computing. The central component of grid is resource management system (RMS). The main functions of RMS are scheduling and allocation of subtasks. The goal of this paper is to provide an optimal learning solution for dynamically choosing appropriate resource. In this paper we introduce an intelligent approach to schedule subtasks based on reinforcement learning. That is named LESG. In LESG a flexible allocation according to subtasks and resources attributes, increases performance of gird.