Service Provisioning through Opportunistic Computing in Mobile Clouds

Mobile clouds are a new paradigm enabling mobile users to access the heterogeneous services present in a pervasive mobile environment together with the rich service offers of the cloud infrastructures. In mobile computing environments mobile devices can also act as service providers, using approaches conceptually similar to service-oriented models. Many approaches implement service provisioning between mobile devices with the intervention of cloud-based handlers, with mobility playing a disruptive role to the functionality offered by of the system. In our approach, we exploit the opportunistic computing model, whereby mobile devices exploit direct contacts to provide services to each other, without necessarily go through conventional cloud services residing in the Internet. Conventional cloud services are therefore complemented by a mobile cloud formed directly by the mobile devices. This paper exploits an algorithm for service selection and composition in this type of mobile cloud environments able to estimate the execution time of a service composition. The model enables the system to produce an estimate of the execution time of the alternative compositions that can be exploited to solve a user's request and then choose the best one among them. We compare the performance of our algorithm with alternative strategies, showing its superior performance from a number of standpoints. In particular, we show how our algorithm can manage a higher load of requests without causing instability in the system conversely to the other strategies. When the load of requests is manageable for all strategies, our algorithm can achieve up to 75% less time spent in average to solve requests

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure