Several relevant research and innovation activities have recently investigated the technical and economic advantages of cloud computing for the provisioning of telco service infrastructures, in particular towards all-IP next generation 5G networks. In fact, the evolution of telco service infrastructures traditionally requires a significant upfront investment (and a long adoption process). Conversely, cloud exploitation significantly lowers investment risks by potentially providing elasticity in service provisioning via flexible Virtual Network Functions (VNFs) on top of a Network Functions Virtualization (NFV) Infrastructure. In this context, the paper presents novel solutions that we have designed, implemented, and evaluated within the EU FP7 Mobile Cloud Networking project (MCN). Their aim is to achieve cost-effective elastic provisioning of telco services over heterogeneous and federated cloud providers, with the specific focus of supporting the extreme quality levels that are demanded by traditional, non-virtualized, and dedicated telco infrastructures. In particular, we concentrate on how to effectively and efficiently automate service state migration for coarse-grained telco service (cloudified) components by leveraging industry-mature orchestration technologies and cloud management frameworks. While our proposed state migration model and procedure are general, its implementation is experimented for MCN's Rating, Charging, and Billing as a Service (RCBaaS). This MCN functionality has been chosen by purpose due to its challenging reliability and uptime requirements. The reported experimental and simulation results show the technical feasibility of the proposed solution under different and realistic load conditions for next-generation and cloudified 5G services.

Mobile computation offloading has recently attracted much interest and first offloading solutions have been developed. However, the relevant technical challenge of how to automatically determine offloadable sections of Android applications has not been adequately investigated so far. This paper proposes an innovative task selection algorithm that can parse an Android application autonomously and classify all the methods based on their offloadability by adopting a fine grained and multi-steps analyzer. The reported experimental results show the effectiveness of our solution when applied to the top 25 most downloaded Android apps on the Google Play store, by showing its accuracy in identifying off loadable methods and demonstrating the potential benefits of automated mobile computation offloading.

First research activities are starting to recognize the suitability of edge computing approaches in several Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) application domains, for instance, to better address scalability issues and to improve reactivity via local control decisions and actuation. The paper originally proposes the design and implementation of an extended edge computing platform, specifically designed for the support of mobile services when provisioned in so-called hostile environments. In particular, our original platform is capable of proactive migration of virtualized functions in response to provision-time device mobility. The paper provides a practical contribution in terms of design and implementation of proactive migration as an extension of the Elijah platform, available for the community of researchers/practitioners in the field. In addition, the paper contributes with lessons learnt from deployment and experimentation, including experimental results about the performance achievable when adopting simple but effective strategies for proactive migration.