A Study Of Hadoop-based Test For Detection Of Total Planned Completion Time Minimization

Currently, the country's growing demand for power transmission and distribution equipment, the experimental testing business of domestic transmission and distribution industry surge, at the same time the customer demand that the capacity, cycle and cost of experimental testing services is increasing. Effective test for detection program can enhance the ability to optimize the allocation of resources and improve the efficiency of enterprises trial testing services to meet the growing demand for testing services. The traditional test planning mainly depends on the person's subjective experience. Due to the uncertainty and complexity of the testing process modeling test detection process, making the development of tests to detect the effect of planned programs are generally not ideal, and due to the continuous expansion of business scale test for detection, and further increase the difficulty of the development test program testing program, which test the detection efficiency of enterprises and the quality of presented unprecedented challenges.In response to these problems, we proceed from the experimental detection programs. First of all, for the product test plan scheduling problems, to the total completion time minimization trial testing program as the goal proposed test program testing program optimization based on genetic algorithms, and algorithms are implemented to give a satisfactory test scheduling results. Secondly, in the face of expanding business scale trials testing carried out tht research of Hadoop-based parallel genetic algorithm, realized the coarse-grained parallel genetic algorithm based on Map Reduce, and applied experimental test plan, with the traditional genetic algorithm model results comparative analysis, to verify the performance of parallel genetic algorithm. The main research work is as follows:1. For the product test plan scheduling problem, this paper presents a optimization scheme of minimizing the total completion time of trial test plan.2. This paper implements the test plan optimization scheme based on genetic algorithm by MATLAB, and verifies the performance of the algorithm on the basis of the test data in high voltage electrical appliances.3. For the massive test business data, this paper realizes the parallel genetic algorithm based on Map Reduce, and implements and verifies the performance of the algorithm.