Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm

Abstract

For applications in large-scale distributed systems, it is becoming increasingly important to provide reliable scheduling by evaluating the reliability of resources. However, most existing reputation models used for reliability evaluation ignore the critical influence of task runtime. In addition, most previous work uses list heuristics to optimize the makespan and reliability of workflow applications instead of genetic algorithms (GAs), which can give several satisfying solutions for choice. Hence, in this paper, we first propose the reliability-driven (RD) reputation, which is time dependent, and can be used to effectively evaluate the reliability of a resource in widely distributed system..