An Autonomic Framework for Mobile Cloud Computing

This thesis presents the formal definition of a novel Mobile Cloud Computing (MCC) extension of the Networked Autonomic Machine (NAM) framework, a general-purpose conceptual tool which describes large-scale distributed autonomic systems. The introduction of autonomic policies in the MCC paradigm has proved to be an effective technique to increase the robustness and flexibility of MCC systems. In particular, autonomic policies based on continuous resource and connectivity monitoring help automate context-aware decisions for computation offloading. We have also provided NAM with a formalization in terms of a transformational operational semantics in order to fill the gap between its existing Java implementation NAM4J and its conceptual definition. Moreover, we have extended NAM4J by adding several components with the purpose of managing large scale autonomic distributed environments. In particular, the middleware allows for the implementation of peer-to-peer (P2P) networks of NAM nodes. Moreover, NAM mobility actions have been implemented to enable the migration of code, execution state and data. Within NAM4J, we have designed and developed a component, denoted as context bus, which is particularly useful in collaborative applications in that, if replicated on each peer, it instantiates a virtual shared channel allowing nodes to notify and get notified about context events. Regarding the autonomic policies management, we have provided NAM4J with a rule engine, whose purpose is to allow a system to autonomously determine when offloading is convenient. We have also provided NAM4J with trust and reputation management mechanisms to make the middleware suitable for applications in which such aspects are of great interest. To this purpose, we have designed and implemented a distributed framework, denoted as DARTSense, where no central server is required, as reputation values are stored and updated by participants in a subjective fashion. We have also investigated the literature regarding MCC systems. The analysis pointed out that all MCC models focus on mobile devices, and consider the Cloud as a system with unlimited resources. To contribute in filling this gap, we defined a modeling and simulation framework for the design and analysis of MCC systems, encompassing both their sides. We have also implemented a modular and reusable simulator of the model. We have applied the NAM principles to two different application scenarios. First, we have defined a hybrid P2P/cloud approach where components and protocols are autonomically configured according to specific target goals, such as cost-effectiveness, reliability and availability. Merging P2P and cloud paradigms brings together the advantages of both: high availability, provided by the Cloud presence, and low cost, by exploiting inexpensive peers resources. As an example, we have shown how the proposed approach can be used to design NAM-based collaborative storage systems based on an autonomic policy to decide how to distribute data chunks among peers and Cloud, according to cost minimization and data availability goals. As a second application, we have defined an autonomic architecture for decentralized urban participatory sensing (UPS) which bridges sensor networks and mobile systems to improve effectiveness and efficiency. The developed application allows users to retrieve and publish different types of sensed information by using the features provided by NAM4J's context bus. Trust and reputation is managed through the application of DARTSense mechanisms. Also, the application includes an autonomic policy that detects areas characterized by few contributors, and tries to recruit new providers by migrating code necessary to sensing, through NAM mobility actions.