Algorithms developed for scheduling applications on heterogeneous multiprocessor system focus on a single objective such as execution time, cost or total data transmission time. However, if more than one objective (e.g. execution cost and time, which may be in conflict) are considered, then the problem becomes more challenging. This project is proposed to develop a multiobjective scheduling algorithm using Evolutionary techniques for scheduling a set of dependent tasks on available resources in a multiprocessor environment which will minimize the makespan and reliability cost. A Non-dominated sorting Genetic Algorithm-II procedure has been developed to get the pareto-optimal solutions. NSGA-II is a Elitist Evolutionary algorithm, and it takes the initial parental solution without any changes, in all iteration to eliminate the problem of loss of some pareto-optimal solutions.NSGA-II uses crowding distance concept to create a diversity of the solutions.

Cloud computing is an emerging technology in distributed computing which provides pay per use according to user demand and requirement. The primary aim of the Cloud computing is to provide efficient access to distributed resources. Scheduling of task is a critical issue in cloud computing, because it serves many users. The An approach for categorizing the tasks as Hard Real-Time Tasks (critical tasks that need to be completed on time with high rates of confidentiality) and Soft Real-Time Tasks (tasks that can be completed with certain delay and still can be efficient in its own way) before they are scheduled is applied. From the results observed the efficient processor for a particular combination of the tasks is determined thus producing customized results for each of the tasks. Efficient task scheduling is of high criticality for obtaining high performance in heterogeneous multiprocessor systems. Since task scheduling is a NP-hard problem, The Genetic Algorithm, an Evolutionary Algorithm which make use of techniques inspired by evolutionary biology such as inheritance, mutation, selection and crossover that is capable of producing optimal solutions.

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