원문정보
초록
영어
In recent years, distributed computing technology has been one of the cutting edge technologies for its low power and cost, which makes numerous IT organizations extend their hands in order to improve their financial ability. Because of these new features, grid computing, the original task scheduling mechanism, can’t work effectively in distributed computing environments, hence, we need a new task scheduling method to solve the problems. With considering the complex characters of the task in different distributed computing applications, firstly, we construct a more comprehensive task scheduling model, which has three sub objective functions. Secondly, we present an improved genetic algorithm to solve the multi- objective NP-hard problem. Finally, we implement some simulation experiments, and the evaluation results show us that the proposed model and improved GA are efficient and effective. The first part is the research status and related problems. The second part is the establishment of system architecture and task scheduling model. The last part is the experimental analysis and conclusion.
목차
1. Introduction
2. Collaborative Computing Task Scheduling Model
2.1. Problem Formulation
2.2. Objective Functions and Optimization Model
3. Improved Genetic Algorithm
3.1. To Determine the Encoding Method
3.2. The Population Initialization
3.3. Fitness Calculation and set other Parameters of GA
3.4. The process of Evaluation Model
4. A Numerical Example Analysis
4.1. Sample Data
4.2. Comprehensive Evaluation and Analysis of the Results
5. Conclusion
References