During the rule scheduling process, the rule scheduler is responsible for choosing one of the activated or ready-to-be-executed rules to evaluate its condition section or execute its action section, respectively.
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We compare the results with the Proportional Fair and Exp rule scheduling algorithms.
In our laboratory environment, EX-SJFEsTLA is compared with those rule scheduling approaches that are unconstrained as EX-SJFEsTLA is.
The results of experiments revealed that the proposed approach improved the rule scheduling process according to the evaluation criteria.
Based on the illustration, it can be observed that all of the proposed schemes outperform the EXP rule scheduling scheme.
In this research, we evaluate and compare existing rule scheduling approaches in a laboratory environment based on a three-tier architecture.
There are criteria used for the evaluation and comparison of rule scheduling approaches: Average Response Time, Throughput, Response Time Standard Deviation, Time Overhead per Transaction, and CPU Utilization.
In this paper, a new approach, referred to as EX-SJFEsTLA, is proposed to improve the rule scheduling process, using a learning automaton.
