Although millions of users download and use third-party Android applications from the Google Play store, little information is known on an aggregated level about these applications. We have built PlayDrone, the first scalable Google Play store crawler, and used it to index and analyze over 1,100,000 applications in the Google Play store on a daily basis, the largest such index of Android applications. PlayDrone leverages various hacking techniques to circumvent Google's roadblocks for indexing Google Play store content, and makes proprietary application sources available, including source code for over 880,000 free applications. We demonstrate the usefulness of PlayDrone in decompiling and analyzing application content by exploring four previously unaddressed issues: the characterization of Google Play application content at large scale and its evolution over time, library usage in applications and its impact on application portability, duplicative application content in Google Play, and the ineffectiveness of OAuth and related service authentication mechanisms resulting in malicious users being able to easily gain unauthorized access to user data and resources on Amazon Web Services and Facebook.

Real-tune applications such as multimedia audio and video are increasingly populating the workstation desktop. To support the execution of these applications in conjunction with traditional non-realtime applications, we have created SMART, a Scheduler for Muhimedia And Real-'Hme applications. SMART supports applications with time constraints. and provides dynamic feedback to applications to allow them to adapt to the current load. In addition. the support for real-lime applications is integrated with the support for conventional computations. This allows the user to prioritize across real-time and conventional computations, and dictate how the processor is to be shared among applications of the same priority. As the system load changes, SMART adjusts the allocation of resources dynamically and seamlessly. SMART is unique in its ability to automatically shed real-time tasks and regulate their execution rates when the system is overloaded, while providing better value in underloaded conditions than previously proposed schemes. We have implemented SMART in the Solaris UNIX operating system and measured its performance against 0th~ schedulers in executing real-time, interactive, and batch applications. Our results demonstrate SMART% superior performance in supporting multimedia applications.

Modern thin-client systems are designed to provide the same graphical interfaces and applications available on traditional desktop computers while centralizing administration and allowing more efficient use of computing resources. Despite the rapidly increasing popularity of these client-server systems, there are few reliable analyses of their performance. Industry standard benchmark techniques commonly used for measuring desktop system performance are ill-suited for measuring the performance of thin-client systems because these benchmarks only measure application performance on the server, not the actual user-perceived performance on the client. To address this problem, we have developed slow-motion benchmarking, a new measurement technique for evaluating thin-client systems. In slow-motion benchmarking, performance is measured by capturing network packet traces between a thin client and its respective server during the execution of a slow-motion version of a conventional benchmark application. These results can then be used either independently or in conjunction with conventional benchmark results to yield an accurate and objective measure of the performance of thin-client systems. We have demonstrated the effectiveness of slow-motion benchmarking by using this technique to measure the performance of several popular thin-client systems in various network environments on Web and multimedia workloads. Our results show that slow-motion benchmarking solves the problems with using conventional benchmarks on thin-client systems and is an accurate tool for analyzing the performance of these systems.

While many application service providers have proposed using thin-client computing to deliver computational services over the Internet, little work has been done to evaluate the effectiveness of thin-client computing in a wide-area network. To assess the potential of thin-client computing in the context of future commodity high-bandwidth Internet access, we have used a novel, noninvasive slow-motion benchmarking technique to evaluate the performance of several popular thin-client computing platforms in delivering computational services cross-country over Internet2. Our results show that using thin-client computing in a wide-area network environment can deliver acceptable performance over Internet2, even when client and server are located thousands of miles apart on opposite ends of the country. However, performance varies widely among thin-client platforms and not all platforms are suitable for this environment. While many thin-client systems are touted as being bandwidth efficient, we show that network latency is often the key factor in limiting wide-area thin-client performance. Furthermore, we show that the same techniques used to improve bandwidth efficiency often result in worse overall performance in wide-area networks. We characterize and analyze the different design choices in the various thin-client platforms and explain which of these choices should be selected for supporting wide-area computing services.