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Scheduling Model for Symmetric Multiprocessing Architecture Based on Process Behavior

this paper presents a new method for scheduling of symmetric multiprocessing (SMP) architecture based on process behavior. The method takes advantage of process behavior, which includes system calls to create groups of similar processes using machine-learning techniques like clustering or classification, and then makes process distribution decisions based on classification or clustering groups. The new method is divided into three stages: the first phase is collecting data about process and defining subset of data is to be used in further processing. The second phase is using data collected in classification or clustering to create classification/clustering models by applying common techniques similar to those used in machine learning, such as a decision tree for classification or EM for clustering. System training classification should be done in this phase, and after that, classification or clustering models should be applied on a running system to find out in which group each process belongs. The third phase is using process groups as a parameter of scheduling on SMP (sympatric Multi Processor) systems when doing distribution over multi- processor cores. Another advantage can be achieved by letting the end user train the system to classify a specific type of process and assign it to a specific process core, targeting real-time response or performance gain. The new method increases process performance and decreases response time based on different kinds of distribution.