A new genetic algorithm based scheduling for volunteer computing

For an application in volunteer computing environments, providing a reliable scheduling based on resource reliability evaluation is becoming increasingly important. Most existing reputation models used for reliability evaluation ignore the task runtime influence. Moreover, to optimize makespan and reliability for workflow applications, most existing works use list heuristics rather than genetic algorithms (GAs) which can usually give better solutions. Hence, in this paper, we propose a look-ahead genetic algorithm (LAGA) to optimize both time and reliability for a workflow application. LAGA uses a novel evolution and evaluation mechanism: the evolution operators evolve the task-resource mapping for a scheduling solution, while the solution´s task order is determined in the evaluation step using our proposed max-min strategy, which is the first two phase strategy that can work with GAs. The experiments show that LAGA can provide better solutions than existing list heuristics and evolve to better solutions more quickly than a traditional genetic algorithm.