The base motivation of Mobile Cloud Computing was empowering mobile devices by application offloading onto powerful cloud resources. However, this goal can't entirely be reached because of the high offloading cost imposed by the long physical distance between the mobile device and the cloud. To address this issue, we propose an application offloading onto a nearby mobile cloud composed of the mobile devices in the vicinity-a Spontaneous Proximity Cloud. We introduce our proposed dynamic, ant-inspired, bi-objective offloading middleware-ACOMMA, and explain its extension to perform a close mobile application offloading. With the learning-based offloading decision-making process of ACOMMA, combined to the collaborative resource sharing, the mobile devices can cooperate for decision cache sharing. We evaluate the performance of ACOMMA in collaborative mode with real benchmarks Face Recognition and Monte-Carlo algorithms-and achieve 50 execution time gain.
Mobile phones are set to become the universal interface to online services and cloud computing applications. However, using them for this purpose today is limited to two configurations: applications either run on the phone or run on the server and are remotely accessed by the phone. These two options do not allow for a customized and flexible service interaction, limiting the possibilities for performance optimization as well. In this paper we present a middleware platform that can automatically distribute different layers of an application between the phone and the server, and optimize a variety of objective functions (latency, data transferred, cost, etc.). Our approach builds on existing technology for distributed module management and does not require new infrastructures. In the paper we discuss how to model applications as a consumption graph, and how to process it with a number of novel algorithms to find the optimal distribution of the application modules. The application is then dynamically deployed on the phone in an efficient and transparent manner. We have tested and validated our approach with extensive experiments and with two different applications. The results indicate that the techniques we propose can significantly optimize the performance of cloud applications when used from mobile phones. Despite the increased capabilities of mobile devices, mobile application resource requirements can often transcend what can be accomplished on a single device. This has been addressed through several proposals for efficient computation offloading from mobile devices to remote cloud resources or closely located computing resources known as cloudlets. In this paper we consider an environment in which computational offloading is performed among a set of mobile devices. We call this environment a Mobile Device Cloud (MDC). We are interested in MDCs where nodes are highly collaborative . We develop computational offloading schemes that maximize the lifetime of the ensemble of mobile devices where we consider the network to be alive as long as no device has depleted its battery. As a secondary contribution in this work, we develop and use an experimentation platform that allows us to evaluate a range of computational models and profiles derived from a realistic testbed. We use this platform as a first step in an evaluation exercise that demonstrates the effectiveness of our computation offloading algorithms in extending the lifetime of an MDC. When we think of mobile cloud computing today, we typically refer to empowering mobile devices - in particular smartphones and tablets - with the capabilities of stationary resources residing in giant data centers. But what happens when these mobile devices become as powerful as our personal computers or more? This paper presents our vision of a future in which mobile devices become a core component of mobile cloud computing architectures. We envision a world where mobile devices will be capable of forming mobile clouds, or mClouds, to accomplish tasks locally without relying, when possible, on costly and, sometimes, inefficient backend communication. We discuss a possible mClouds architecture, its benefits and tradeoffs, and the user incentive scheme to support the mCloud design. Mobile devices are evolving into powerful systems due to recent advances in their communication, storage and computation technologies. They are poised to play a key role in providing a rich collaborative computing platform for various applications. This paper proposes "Transient Clouds" — a collaborative computing platform that allows nearby devices to form an ad-hoc network and provide various capabilities as cloud services. Transient Clouds utilize the collective capabilities of the devices present, along with their social and context awareness that cannot be provided efficiently by the traditional clouds. We present a modified algorithm of the Hungarian method for assigning tasks to devices in order to achieve various goals (e.g., load balancing, collocating executions, etc.). We evaluate the performance of our proposed algorithms through simulation and provide a real implementation on the Android platform using the Wi-Fi Direct framework. We envision Transient Clouds to be utilized in temporal scenarios in which the cloud is created on-the-fly by the devices present in an environment and would disappear as the devices leave the network. 