Research On P2P Multi-objective Constraints Task Scheduling Based On Immune Algorithm

With peer-to-peer computing model rising, construct highly scalable, highly reliable, high-performance distributed computing system which research is a hot one in recent years. Task scheduling is a key technology in P2P computing, which direct impact on the computing performance of the entire system. P2P task scheduling is that assigning a group of parallel processing tasks from one node to more nodes, with a view to obtaining a better system performance. As the issue can not obtain optimal solution in polynomial time, it has been recognized as an NP-complete problem.For the NP-complete problems, in recent years the rising immune algorithm is a better solution, that is, in a short period of time to find a better solution. Many experts on distributed systems began concerning immune algorithm research. The algorithm have unique advantages in solving complex issues, such as large space, nonlinear, global search excellence and so on, but the traditional methods don't where in task scheduling.An immune algorithm for task scheduling in P2P environment was proposed to resolve the weakness in genetic algorithm. After some definitions were made, the population initialization operator and clone selection operator considering load balance and population diversity were described, and the new adaptive variation operator and vaccine with apriori knowledge were designed. Meanwhile, the strategies for finding and managing P2P peers were given. Based on it, the task scheduling process was completed according to the proposed strategy. Experimental results showed the effectiveness of this strategy.The features of the P2P network make its task scheduling performance constrain by multi-factor, the task scheduling with multi-objective constraints is presented by using immune algorithm.The clone selection operator controlled by entropy, the new crossover operator, mutation operator and vaccine with apriori knowledge are designed for task scheduling based on model definition. And then the multi-objective task scheduling strategy is proposed after describing the mechanism for searching and managing the available P2P nodes. Experimental results indicate the validity of the proposed scheduling strategy in shortening the execution time and communication time, as well as saving the scheduling costs.