Abstract-Smartphone is becoming a key element in providing greater user access to the mobile Internet. Many complex applications, which are used to be only on PCs, have been developed and run on smartphones. These applications extend the functionalities of smartphones and make them more convenient for users to be connected. However, they also greatly increase the power consumption of smartphones and many users are frustrated with the long delay of web browsing when using smartphones. In this paper, we have discovered that the key reason of the long delay and high power consumption in web browsing is not due to the bandwidth limitation most of time in 3G networks. The local computation limitation at the smartphone is the real bottleneck for opening most webpages. To address this issue, we propose an architecture, called Virtual-Machine based Proxy (VMP), to shift the computing from smartphones to the VMP. To illustrate the feasibility of deploying the proposed VMP system in 3G networks, we have built a prototype using Xen virtual machines and Android Phones with AT&T UMTS network. Experimental results show that compared to normal smartphone browser, our VMP approach reduces the delay by more than 80% and reduces the power consumption during web browsing by more than 45%.
Abstract-Cloud computing has drawn increasing attention from the scientific computing community due to its ease of use, elasticity, and relatively low cost. Because a high-performance computing (HPC) application is usually resource demanding, without careful planning, it can incur a high monetary expense even in Cloud. We design a tool called CAP 3 (Cloud AutoProvisioning framework for Parallel Processing) to help a user minimize the expense of running an HPC application in Cloud, while meeting the user-specified job deadline. Given an HPC application, CAP 3 automatically profiles the application, builds a model to predict its performance, and infers a proper cluster size that can finish the job within its deadline while minimizing the total cost. To further reduce the cost, CAP 3 intelligently chooses the Cloud's reliable on-demand instances or low-cost spot instances, depending on whether the remaining time is tight in meeting the application's deadline. Experiments on Amazon EC2 show that the execution strategy given by CAP 3 is costeffective, by choosing a proper cluster size and a proper instance type (on-demand or spot).
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