Hybrid model for tasks scheduling in distributed real time system

Resource allocation and their scheduling to optimize performance measures in heterogeneous environments are famous such as an NP-hard issue, not only for the resource heterogeneity, but also for the possibility of applying allocation to take advantage of idle resource. This article proposes a scheduling technique for communicating tasks by using two hybrid genetic algorithms (HGAs) which minimizes system cost and response time and maximizes the reliability of the distributed real time system. In the present technique, convergence of genetic algorithm (GA) is made better by offering new encoding and population initialization method and genetic operations. This technique is completed in two phases: Phase I develops hybrid c-mean genetic algorithm (HCMGA) which is a fusion of fuzzy c-means (FCM) technique and genetic algorithm (GA) and Phase II develops hybrid branch and bound genetic algorithm (HBBGA) which is a fusion of branch and bound (B&B) technique and genetic algorithm (GA). HCMGA, makes ‘s’ clusters of ‘r’ tasks by using FCM clustering technique then these clusters are updated by using GA to get final clusters of tasks. HBBGA, initially allocates clusters of tasks onto processors by B&B technique then their allocations are updated by using GA to get the final allocation. To check the performance of the proposed technique, several examples are considered from different research articles and results of the numerical examples have compared with well-regard existing models. The proposed technique is able to outperform all comparative techniques established in the literature; thus, superior results are obtained. This technique is suitable for arbitrary number of processors and tasks.