Web browsing is always a key requirement of Internet users. Current mobile Web apps can contain computation-intensive JavaScript logics and thus affect browsing performance. Learning from our over-decade research and development experiences of the Internetware paradigm, we present the novel and generic i-Jacob approach to improving the performance of mobile Web browsing with effective JavaScript-code offloading. Our approach proposes a programming abstraction to make mobile Web situational and adaptive to contexts, by specifying the computation-intensive and “ offloadable ” code, and develops a platform-independent lightweight runtime spanning the mobile devices and the cloud. We demonstrate the efficiency of i-Jacob with some typical computation-intensive tasks over various combinations of hardware, operating systems, browsers, and network connections. The improvements can reach up to 49× speed-up in response time and 90% saving in energy.

prevalent smartphones have become the major entrance to accessing services on the Internet. On smartphones, users can have two options as the clients, i.e., native apps and Web apps. There have been several debates about native apps and Web apps. However, major service providers such as Google, Amazon, and Facebook provide both native apps and Web apps to end-users. Essentially, the performance differences between these two types of apps haven't been addressed. Indeed, the performance differences make non-trivial impacts on apps development, deployment, and distribution. In this article, we conduct a measurement study on the performance of native apps and Web apps on Android smartphones. Specifically, we want to explore given the same functionalities, do Web apps always perform poorly compared to native apps. We select 328 services from some popular providers, covering various domains such as e-commerce, map, social networking, and entertainment. With HTTP-level trace analysis, we demystify the workflows on how native apps and Web apps deliver services on mobile devices, respectively. Then, we characterize the performance differences between native apps and Web apps with the metrics including the number of requests, response time, data drain, and energy consumption. We find that the performance of Web apps is better than native apps in more than 31 percent cases. Our derived knowledge can suggest some recommendations to improve the performance for mobile apps.

App marketplaces host millions of mobile apps that are downloaded billions of times. Investigating how people manage mobile apps in their everyday lives creates a unique opportunity to understand the behavior and preferences of mobile device users, infer the quality of apps, and improve user experience. Existing literature provides very limited knowledge about app management activities, due to the lack of app usage data at scale. This article takes the initiative to analyze a very large app management log collected through a leading Android app marketplace. The dataset covers 5 months of detailed downloading, updating, and uninstallation activities, which involve 17 million anonymized users and 1 million apps. We present a surprising finding that the metrics commonly used to rank apps in app stores do not truly reflect the users’ real attitudes. We then identify behavioral patterns from the app management activities that more accurately indicate user preferences of an app even when no explicit rating is available. A systematic statistical analysis is designed to evaluate machine learning models that are trained to predict user preferences using these behavioral patterns, which features an inverse probability weighting method to correct the selection biases in the training process.

Numerous complains have been made by Android users who severely suffer from the sluggish response when interacting with their devices. However, very few studies have been conducted to understand the user-perceived latency or mitigate the UI-lagging problem. In this paper, we conduct the first systematic measurement study to quantify the user-perceived latency using typical interaction-intensive Android apps in running with and without background workloads. We reveal the insufficiency of Android system in ensuring the performance of foreground apps and therefore design a new system to address the insufficiency accordingly. We develop a lightweight tracker to accurately identify all delay-critical threads that contribute to the slow response of user interactions. We then build a resource manager that can efficiently schedule various system resources including CPU, I/O, and GPU, for optimizing the performance of these threads. We implement the proposed system on commercial smartphones and conduct comprehensive experiments to evaluate our implementation. Evaluation results show that our system is able to significantly reduce the user-perceived latency of foreground apps in running with aggressive background workloads, up to 10x, while incurring negligible system overhead of less than 3.1 percent CPU and 7 MB memory.

Imperfect Web resource loading prevents mobile Web browsing from providing satisfactory user experience. In this article, we design and implement the SWAROVsky system to address three main issues of current inefficient Web resource loading: (1) on-demand and thus slow loading of sub-resources of webpages; (2) duplicated loading of resources with different URLs but the same content; and (3) redundant loading of the same resource due to improper cache configurations. SWAROVsky employs a dual-proxy architecture that comprises a remote cloud-side proxy and a local proxy on mobile devices. The remote proxy proactively loads webpages from their original Web servers and maintains a resource loading graph for every single webpage. Based on the graph, the remote proxy is capable of deciding which resources are “really” needed for the webpage and their loading orders, and thus can synchronize these needed resources with the local proxy of a client efficiently and timely. The local proxy also runs an intelligent and light-weight algorithm to identify resources with different URLs but the same content, and thus can avoid duplicated downloading of the same content via network. Our system can be used with existing Web browsers and Web servers, and does not break the normal semantics of a webpage. Evaluations with 50 websites show that on average our system can reduce the page load time by 43.1 percent and the network data transmission by 57.6 percent, while imposing marginal system overhead.