Recent diversification of mobile computing devices allows a mobile user to own multiple types of devices for different application scenarios, but also results in various restrictions on the performance and usability of these devices. A viable solution to such restriction is to incorporate and interconnect mobile devices towards a personal mobile cloud where these devices can complement each other via cooperative resource sharing, but is challenging due to the heterogeneity of mobile devices in both hardware and software aspects. In this paper, we propose a novel design of resource sharing framework to address these challenges and generically interconnect heterogeneous mobile devices. Our basic idea is to mask the hardware and software heterogeneity in mobile systems by exploiting the existing mobile OS services as the interface of resource sharing, and further develop the resource sharing framework as a middleware in the mobile OS. We have implemented our design over various mobile platforms with diverse characteristics and resource limits, and demonstrated that our design can efficiently support generic resource sharing among heterogeneous mobile devices without incurring significant system overhead or requiring individual system modification.

Virtual Reality (VR) improves the user's experience when interacting with the virtual world, and could revolutionarily transform the designs of many interactive systems. However, providing VR from untethered mobile devices is difficult due to their limited local capabilities. Existing VR solutions address this difficulty by rendering VR frames at remote computing facilities, but are limited to rendering every VR frame separately. A tremendous amount of VR frame data, hence, needs to be transmitted to mobile devices over low-bandwidth wireless links and seriously impairs VR performance. In this paper, we aim to remove this performance constraint on highly dynamic VR applications with complicated scenes and intensive user movement, by adaptively reusing the redundant VR pixels across multiple VR frames. We leverage the unique characteristics of image warping used in current VR applications, and fundamentally expand the scope of image warping to the entire VR lifespan to precisely capture the fluctuations of VR scene due to VR dynamics. We implemented our design over Android OS and Unity VR application engine, and demonstrated that our design can maximize the mobile VR performance over highly dynamic VR scenarios with 95% less amount of VR frame data being transmitted, by completely removing the pixel redundancy across VR frames.

Recent diversification of mobile computing devices allows a mobile user to own multiple types of devices for different application scenarios, but also results in various restrictions on the performance and usability of these devices. A viable solution to such restriction is to incorporate and interconnect mobile devices towards a personal mobile cloud where these devices can complement each other via cooperative resource sharing, but is challenging due to the heterogeneity of mobile devices in both hardware and software aspects. In this paper, we propose a novel design of resource sharing framework to address these challenges and generically interconnect heterogeneous mobile devices. Our basic idea is to mask the hardware and software heterogeneity in mobile systems by exploiting the existing mobile OS services as the interface of resource sharing, and further develop the resource sharing framework as a middleware in the mobile OS. We have implemented our design over various mobile platforms with diverse characteristics and resource limits, and demonstrated that our design can efficiently support generic resource sharing among heterogeneous mobile devices without incurring significant system overhead or requiring individual system modification.

Mobile Cloud Computing (MCC) is of particular importance to address the conflict between the increasing complexity of user applications and the limited lifespan of mobile device's battery, by offloading the computational workloads from local devices to the remote cloud. Current offloading schemes either require the programmer's annotations, which restricts its wide application; or transmits too much unnecessary data, resulting bandwidth, and energy waste. In this paper, we propose a novel method-level offloading methodology to offload local computational workload with as least data transmission as possible. Our basic idea is to identify the contexts which are necessary to the method execution by parsing application binaries in advance and applying this parsing result to selectively migrate heap data while allowing successful method execution remotely. To further improve the efficiency of such offline parsing of application binaries, our scheme also conducts one-time parsing to all the mobile OS libraries and reuses these parsing results for different user applications. We have implemented our design over the Dalvik Virtual Machine of Android OS. Our experiments and evaluation against applications downloaded from Google Play show that our approach can save data transmission significantly comparing to existing schemes.

The performance of mobile computing would be significantly improved by leveraging cloud computing and migrating mobile workloads for remote execution at the cloud. In this paper, to efficiently handle the peak load and satisfy the requirements of remote program execution, we propose to deploy cloud servers at the network edge and design the edge cloud as a tree hierarchy of geo-distributed servers, so as to efficiently utilize the cloud resources to serve the peak loads from mobile users. The hierarchical architecture of edge cloud enables aggregation of the peak loads across different tiers of cloud servers to maximize the amount of mobile workloads being served. To ensure efficient utilization of cloud resources, we further propose a workload placement algorithm that decides which edge cloud servers mobile programs are placed on and how much computational capacity is provisioned to execute each program. The performance of our proposed hierarchical edge cloud architecture on serving mobile workloads is evaluated by formal analysis, small-scale system experimentation, and large-scale trace-based simulations.

Mobile Cloud Computing (MCC) is of particular importance to address the contradiction between the increasing complexity of user applications and the limited lifespan of mobile device's battery, by offloading the computational workloads from local devices to the remote cloud. Current offloading schemes either require the programmer's annotations, which restricts its wide application; or transmits too much unnecessary data, resulting bandwidth and energy waste. In this paper, we propose a novel method-level offloading methodology to offload local computational workload with as least data transmission as possible. Our basic idea is to identify the contexts which are necessary to the method execution by parsing application binaries in advance and applying this parsing result to selectively migrate heap data while allowing successful method execution remotely. Our implementation of this design is built upon Dalvik Virtual Machine. Our experiments and evaluation against applications downloaded from Google Play show that our approach can save data transmission significantly comparing to existing schemes.

Mobile Cloud Computing (MCC) bridges the gap between limited capabilities of mobile devices and the increasing users' demand of mobile multimedia applications, by offloading the computational workloads from local devices to the remote cloud. Current MCC research focuses on making offloading decisions over different methods of a MCC application, but may inappropriately increase the energy consumption if having transmitted a large amount of program states over expensive wireless channels. Limited research has been done on avoiding such energy waste by exploiting the dynamic patterns of applications' run-time execution for workload offloading. In this paper, we adaptively offload the local computational workload with respect to the run-time application dynamics. Our basic idea is to formulate the dynamic executions of user applications using a semi-Markov model, and to further make offloading decisions based on probabilistic estimations of the offloading operation's energy saving. Such estimation is motivated by experimental investigations over practical smart phone applications, and then builds on analytical modeling of methods' execution times and offloading expenses. Systematic evaluations show that our scheme significantly improves the efficiency of workload offloading compared to existing schemes over various smart phone applications.