面向云服务的上下文感知自适应移动中间件

CLOUD SERVICE-ORIENTED CONTEXT-AWARE ADAPTIVE MOBILE MIDDLEWARE

近年来,随着苹果、黑莓等智能移动设备的日益普及,以及云计算在运算、存储能力等方面展现的优势,基于云计算的移动应用越来越得到人们的关注。然而,由于移动环境下有限的电池能量,不稳定的无线网络以及实时变化QoS水平,使得现有的中间件技术不能很好地满足移动终端访问云服务的要求。为此,提出一种面向云服务的上下文感知自适应移动中间件,该中间件通过监视移动设备的资源和上下文,动态发现和自适应云服务。此外,将一种基于模糊认知图的模型应用到自适应策略中,提高了自适应算法的效率和性能。最后,通过实验和仿真来验证该中间件的有效性。

In recent years, smart mobile devices such as iPhones BlackBerrys, etc. have become highly accessible. The predominance of cloud computing opened out in operation and storage capacities attracts increasing attention of people to the cloud computingbased mobile ap plications. However, existing middleware technology can hardly fit well the demands of mobile terminals accessing to cloud computing due to limited battery capacity, unstable wireless networks and timely varying QoS level in mobile environment. Therefore, in this paper we present a cloud servicseoriented contextaware adaptive mobile middleware. It dynamically finds and adapts to the cloud services by monitoring the resources and context of the mobile devices. Besides, we apply a model of fuzzy cognitive maps （FCMs） to adaptive policy, this enhances the efficiency and performance of adaptive algorithm. At last, we validate the effectiveness of the middleware through experiment and simulation.