Optimizing Makespan and Reliability for Workflow Applications With Reputation and Look-Ahead Genetic Algorithm
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 work flow applications instead of Genetic Algorithms (GAs), which can give several satisfying solutions for choice. Hence, in this paper, the authors 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 systems. They, then propose Look-Ahead Genetic Algorithm (LAGA) which utilizes the RD reputation to optimize both makespan and reliability of a work flow application.