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.

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.

Mobile devices with 3G/4G networking often waste energy in the so-called “tail time” during which the radio is kept on even though no communication is occurring. Prior work has proposed policies to reduce this energy waste by batching network requests. However, this work is challenging to apply in practice due to a lack of mechanisms. In response, we have developed DelayDroid, a framework that allows a developer to add the needed policy to existing, unmodified Android applications (apps) with no human effort as well as no SDK/OS changes. This allows such prior work (as well as our own policies) to be readily deployed and evaluated. The DelayDroid compile-time uses static analysis and bytecode refactoring to identify method calls that send network requests and modify such calls to detour them to the DelayDroid run-time. The run-time then applies a policy to batch them, avoiding the tail time energy waste. DelayDroid also includes a cross-app communication mechanism that supports policies that optimize across multiple apps running together, and we propose a policy that does so. We evaluated the correctness and universality of the DelayDroid mechanisms on 14 popular Android apps chosen from the Google App Store. To evaluate our proposed policy, we studied three DelayDroid-enabled apps (weather forecasting, email client, and news client) running together, finding that the DelayDroid mechanisms combined with our policy can reduce 3G/4G tail time energy waste by 36%.

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.

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.

Redundant transfer of resources is a critical issue for compromising the performance of mobile Web applications (a.k.a., apps) in terms of data traffic, load time, and even energy consumption. Evidence demonstrates that the current cache mechanisms are far from satisfactory. With lessons learned from how native apps manage their resources, in this article, we present the ReWAP approach to fundamentally reducing redundant transfers by restructuring the resource loading of mobile Web apps. ReWAP is based on an efficient resource-packaging mechanism where stable resources are encapsulated and maintained into a package, and such a package shall be loaded always from the local storage and updated by explicitly refreshing. By retrieving and analyzing the update of resources, ReWAP maintains resource packages that can accurately identify which resources can be loaded from the local storage for a considerably long period. ReWAP also provides a wrapper for mobile Web apps to enable loading and updating resource packages in the local storage as well as loading resources from resource packages. ReWAP can be easily and seamlessly deployed into existing mobile Web architectures with minimal modifications, and is transparent to end-users. We evaluate ReWAP based on continuous 15day access traces of 50 mobile Web apps randomly chosen from Alexa top 500 ranking list. Compared to the original mobile Web apps with cache enabled, ReWAP can significantly reduce the data traffic, with the median saving up to 51 percent. In addition, ReWAP can incur only very minor runtime overhead of the client-side browsers and thus does not compromise user experiences.

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.

In enterprise organizations, the Bring-Your-Own-Device (BYOD) requirement has become prevalent as employees use their own mobile devices to process the workflow-oriented tasks. Consequently, it calls for approaches that can quickly develop and integrate mobile user interactions into existing business processes, and adapt to various contexts. However, designing, developing, and deploying adaptive and mobile-oriented user interfaces for existing process engines are non-trivial, and require significant systematic efforts. To address this issue, we present a novel middleware-based approach, called MUIT, to developing and deploying the Mobility, User Interactions and Tasks into WS-BPEL engines. MUIT provides a Domain-Specific Language (DSL) that provides some intuitive facilities to support the declarative development of adaptive, mobile-oriented, and Web-based user interfaces in WS-BPEL. The DSL can significantly reduce developers' manual efforts of developing user interactions by preventing arbitrarily mixed code, and its runtime supports satisfactory user experiences. Additionally, MUIT can be seamlessly integrated into WS-BPEL without intrusions of existing process instances. We implement a proof-of-concept prototype by integrating MUIT into the commodity WS-BPEL-based Apusic Platform, and evaluate the performance and usability of MUIT platform.

The web browser is one of the most significant applications on mobile devices such as smartphones. However, the user experience of mobile web browsing is undesirable because of the slow resource loading. To improve the performance of web resource loading, client-side cache has been adopted as a key mechanism. However, the existing passive measurement studies cannot comprehensively characterize the “client-side” cache performance of mobile web browsing. For example, most of these studies mainly focus on client-side implementations but not server-side configurations, suffer from biased user behaviors, and fail to study “miscached” resources. To address these issues, in this article, we present a proactive approach to making a comprehensive measurement study on client-side cache performance. The key idea of our approach is to proactively crawl resources from hundreds of websites periodically with a fine-grained time interval. Thus, we are able to uncover the resource update history and cache configurations at the server side, and analyze the cache performance in various time granularities. Based on our collected data, we build a new cache analysis model and study the upper bound of how high percentage of resources could potentially be cached and how effectively the caching works in practice. We report detailed analysis results of different websites and various types of web resources, and identify the problems caused by unsatisfactory cache performance. In particular, we identify two major problems - Redundant Transfer and Miscached Resource, which lead to unsatisfactory cache performance. We investigate three main root causes: Same Content, Heuristic Expiration, and Conservative Expiration Time, and discuss what mobile web developers can do to mitigate those problems.

Mobile Web applications (a.k.a., Web apps) stand for an important trend for next-generation Internet-based software. Currently popular mobile Web apps need to be adapted to various and ever-changing contexts and personalized user requirements. Based on our over-decade research experiences and practice on the Internetware paradigm, this position article describes an Internetware-oriented approach to designing, developing, and deploying situational mobile Web apps, by synthesizing the resources and services of mobile and cloud. Guided by a novel Service-Model-View-Controller (SMVC) software model, a mobile Web app is organized into a well-defined structure that facilitates adaptation including online/offline data access, computation offloading, user interface optimization, hybrid composition, etc. We provide efficient runtime support spanning mobile and cloud to make mobile Web apps more flexibly adaptive. The proof-of-concept evaluation demonstrates that our approach can benefit end-users with optimized user experience of mobile Web apps.