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

Opportunistic Service Provisioning in Mobile Clouds of Users' Personal Devices

Opportunistic computing is the recent application of delay-tolerant networking to the creation of networks of mobile devices that give users the capability to share and access services provided by other (mobile) devices in proximity without using any cellular infrastructures. The importance of this paradigm becomes apparent given the ubiquitous proliferation of personal mobile devices in recent years. Opportunistic computing can also be used to realise service offloading, a recent trend in mobile networking research where resources on the edge of the cellular network are used in synergy with the cloud infrastructure. The importance of this application of opportunistic computing comes from the data traffic generated by mobile devices that, in the last few years, has been steadily increasing. While the development of LTE and LTE-A will boost cellular network capacity, it is unclear whether this would be enough to support the expected exponential increase in traffic demands in the medium term. Opportunistic techniques can contribute to solve this problem by offloading computation and data access to locally available devices, exploiting unused resources and balancing allocation of users requests to obtain an increase in service provisioning performances and avoiding network congestion. This thesis brings contributions in two different scenarios: the first one is purely opportunistic with the detailing of a distributed system for the establishment and self-organization of mobile service provisioning. The system is established by each device autonomously collecting and using context information to individuate sequential compositions of resources for service provisioning and, thanks to a stochastic model, find the alternative that is expected to result in the lowest service provisioning time. In the second scenario, this thesis presents a solution for the integration of the opportunistic paradigm into a mobile edge system, where service provisioning is orchestrated between mobile devices and a remote cloud system thanks to the collaboration with network base stations local to the mobile devices. In the first scenario, experiments are presented to validate the decision algorithms and the stochastic model they rely on, while in the second scenario, we evaluate the performance gains obtained by using the opportunistic paradigm for service offloading in respect to traditional remote cloud systems

An Ontology-Based Architecture for Service Discovery and Advice System

n this paper we present a novel, ontology-based approach to service discovery, which exploits domain knowledge and semantic service descriptions to guide the service discovery process and provide advice on service selection and instantiation in interoperable adaptive information systems. The proposed system architecture for service discovery and advice has the advantage of providing specific advice at multiple levels of granularity during the service composition process. At the highest level, the system can help to determine what kind of abstract service is required against a contextual functional request. Once all the services that can fulfil the required function are discovered, the advice system can recommend an appropriate concrete service, taking into account both problem characteristics and quality considerations. More specialized, in-depth advice can also be given, for example, on how to initialize and configure the parameters of a service. The approach and prototype have been proposed to demonstrate practical benefits in the framework of the MAIS (multi-channel adaptive information systems) projec