互联网服务环境(Internet of Services)的动态、自治等特征使得服务自身及基于服务的应用都面临着突出的可用性问题.服务监控是提供服务可用性保障的一个有效手段,然而现有的服务监控方法在应对互联网服务监控需求时存在着适用范围单一、灵活性差等问题,难以满足服务可用性保障在监控目标和监控方法等方面的多样性需求.为此,采用模型驱动架构(Model-Driven Architecture)的方法,设计了一个可支持灵活扩展的服务监控模型,允许用户自主定义满足多样化监控需求的服务监测指标和异常处理机制,并实现了一个模型驱动的适应性服务监控系统,该系统能够通过对模型的解释执行来实现具体的服务监控功能.通过与相关工作的对比表明,本文工作在监控模型的扩展性和监控实现的系统支撑角度具有一定的优势.
When workflow task needs several datasets from different locations in cloud,data transfer becomes a challenge.To avoid the unnecessary data transfer,a graphical-based data placement algorithm for cloud workflow is proposed.The algorithm uses affinity graph to group datasets while keeping a polynomial time complexity.By integrating the algorithm,the workflow engine can intelligently select locations in which the data will reside to avoid the unnecessary data transfer during the initial stage and runtime stage.Simulations show that the proposed algorithm can effectively reduce data transfer during the workflow's execution.
