Trust-driven and QoS demand clustering analysis based cloud workflow scheduling strategies
Due to the restrictions that most traditional scheduling strategies only cared about users quality of service (QoS) time or cost requirements, lacked the effective analysis of users real service demand and could not guarantee scheduling security, this paper added trust into workflows QoS target and proposed a novel customizable cloud workflow scheduling model. In order to better analyze different users service requirements and provide customizable services, the new model divided workflow scheduling into two stages: the macro multi-workflow scheduling as the unit of cloud user and the micro single workflow scheduling. It introduced trust mechanism into multi-workflow scheduling level. And in single workflow scheduling level, it classified workflows into time-sensitive, cost-sensitive and balance three types according to different workflows QoS demand parameters using fuzzy clustering method. Based on it, it customized different service strategies for different type. The simulation experiments show that the new schema has some advantages in shortening workflows final completion time, achieving relatively high execution success rate and user satisfaction compared to other kindred solutions.
cloud workflow scheduling strategy customizable service trust mechanism fuzzy clustering qos demand analysis