中文摘要：

针对云工作流执行过程中的用户隐私保护需求,建立了相应的云工作流调度模型,在粒子群优化算法及模拟退火智能优化算法的基础上,通过引入经典表调度算法CPOP中的任务优先级计算策略,提出一种具有隐私与云资源使用成本感知能力的云工作流调度方法 CP-PSO。该方法采用考虑成本因素的上行与下行权重来计算各个工作流任务的优先级,结合隐私保护需求搜索并优化调度方案。通过仿真实验说明了该方法的有效性。

英文摘要：

To meet the needs of protecting user＇s privacy in practical cloud workflow applications,a relevant cloud workflow scheduling model was constructed.Based on the intelligent optimization algorithms of Particle Swarm Optimization（PSO）and Simulated Annealing algorithm（SA）,aprivacy-aware and cost-aware cloud workflow scheduling algorithm named CP-PSO was designed by introducing task priority computing strategy in CPOP.Each workflow＇s task priority was computed by using summation of upward and downward rank values with consideration of execution cost,and the optimal scheduling solution could be obtained by combining user＇s privacy protection needs with intelligent optimization.The simulation experiments showed the effectiveness of the proposed method.