Grid Independent Task Scheduling Multi-Objective Optimization Model and Genetic Algorithm

The characteristic of heterogeneous grid environment results in that the task scheduling is constrained by a number of factors such as the length of scheduling, the performance of security, and the cost of scheduling, etc. Firstly, under the consideration of the task scheduling demand for secure performance. A security benefit function and the corresponding time cost model are constructed. Meanwhile, according to the history behavior of grid resource nodes, a simple and efficient node's credibility dynamic evaluation model is built by using the weighted function. Based on these, a new constrained multi-objective grid task scheduling mode is proposed. In order to solve this model, the relationship between the characteristic of the task and the different performance among nodes is used to define the individual relationship matrix; then the subjection degree function is used to transform each relationship matrix into a fuzzy matrix. According to the different effects of each objective in the final objective, each weight in the final decision-making is determined. In doing so, the multi-objective model is transformed into a single-objective model. Through the design of a new mode crossover operator and an uniform mutation operator, a genetic algorithm called MUGA for the transformed problem is proposed and the convergence of proposed algorithm is proved. Simulation results show that the proposed algorithm is better than the compared ones in terms of the length of the task scheduling, security efficiency value, reliability and scheduling costs.