Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.

The cloud seems to be an excellent companion of mobile systems, to alleviate battery consumption on smartphones and to backup user's data on-the-fly. Indeed, many recent works focus on frameworks that enable mobile computation offloading to software clones of smartphones on the cloud and on designing cloud-based backup systems for the data stored in our devices. Both mobile computation offloading and data backup involve communication between the real devices and the cloud. This communication does certainly not come for free. It costs in terms of bandwidth (the traffic overhead to communicate with the cloud) and in terms of energy (computation and use of network interfaces on the device). In this work we study the fmobile software/data backupseasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we assume an architecture where each real device is associated to a software clone on the cloud. We consider two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user's data and apps is needed. We give a precise evaluation of the feasibility and costs of both off-clones and back-clones in terms of bandwidth and energy consumption on the real device. We achieve this through measurements done on a real testbed of 11 Android smartphones and an equal number of software clones running on the Amazon EC2 public cloud. The smartphones have been used as the primary mobile by the participants for the whole experiment duration.

During the last years, researche'rs have proposed solutions to help smartphones improve execution time and reduce energy consumption by offloading heavy tasks to remote entities. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices, giving birth to the Device-to-Device offloading paradigm. None of these strategies, though, offers any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.

The complexity of social mobile networks, networks of devices carried by humans (e.g. sensors or PDAs) and communicating with short-range wireless technology, makes it hard protocol evaluation. A simple and efficient mobility model such as SWIM reflects correctly kernel properties of human movement and, at the same time, allows to evaluate accurately protocols in this context. In this paper we investigate the properties of SWIM, in particular how SWIM is able to generate social behavior among the nodes and how SWIM is able to model networks with a power-law exponential decay dichotomy of inter contact time and with complex sub-structures (communities) as the ones observed in the real data traces. We simulate three real scenarios and compare the synthetic data with real world data in terms of inter-contact, contact duration, number of contacts, and presence and structure of communities among nodes and find out a very good matching. By comparing the performance of BUBBLE, a community-based forwarding protocol for social mobile networks, on both real and synthetic data traces, we show that SWIM not only is able to extrapolate key properties of human mobility but also is very accurate in predicting performance of protocols based on social human sub-structures.

This paper presents small world in motion (SWIM), a new mobility model for ad hoc networking. SWIM is relatively simple, is easily tuned by setting just a few parameters, and generates traces that look real-synthetic traces have the same statistical properties of real traces in terms of intercontact times, contact duration, and frequency among node couples. Furthermore, it generates social behavior among nodes and models networks with complex social communities as the ones observed in the real traces. SWIM shows experimentally and theoretically the presence of the power-law and exponential decay dichotomy of intercontact times, and, most importantly, our experiments show that predicts very accurately the performance of forwarding protocols for PSNs like Epidemic, Delegation, Spray&Wait, and more complex, social-based ones like BUBBLE. Moreover, we propose a methodology to assess protocols on model with a large number of nodes. To the best of our knowledge, this is the first such study. Scaling of mobility models is a fundamental issue, yet never considered in the literature. Thanks to SWIM, here we present the first analysis of the scaling capabilities of Epidemic Forwarding, Delegation Forwarding, Spray&Wait, and BUBBLE.