Modern mobile systems are featured by their increasing interactivity with users, which however is accompanied by a severe side effect---users constantly suffer from slow UI responsiveness (SUR). To date, the community have limited understandings of this issue for the challenges of comprehensively measuring SUR events on massive mobile devices. As a major Android phone vendor, in this paper we close the knowledge gap by conducting the first large-scale, long-term measurement study on SUR with 47M devices. Our study identifies the critical factors that lead to SUR from the perspectives of device, system, application, and app market. Most importantly, we note that the largest root cause lies in the wide existence of "hogging" apps, which persistently occupy an unreasonable amount of system resources by leveraging the optimistic design of Android process management. We have built on the insights to remodel Android process states by fully considering their time-sensitive transitions and the actual behaviors of processes, with remarkable real-world impact---the occurrences of SUR are reduced by 60%, together with 10.7% saving of battery consumption.

Recent years have witnessed the profound influence of AI technologies on computer gaming. While grandmaster-level AI robots have largely come true for complex games based on heavy back-end support, in practice many game developers crave for participant AI robots (PARs) that behave like average-level humans with inexpensive infrastructures. Unfortunately, to date there has not been a satisfactory solution that registers large-scale use. In this work, we attempt to develop practical PARs (dubbed ParliRobo) showing acceptably humanoid behaviors with well affordable infrastructures under a challenging scenario-a 3D-FPS (first-person shooter) mobile MMOG with real-time interaction requirements. Based on comprehensive real-world explorations, we eventually enable our attempt through a novel ?transform and polish" methodology. It achieves ultralight implementations of the core system components by non-intuitive yet principled approaches, and meanwhile carefully fixes the probable side effect incurred on user perceptions. Evaluation results from large-scale deployment indicate the close resemblance (96% on average) in biofidelity metrics between ParliRobo and human players; moreover, in 73% mini Turing tests ParliRobo cannot be distinguished from human players.

Delta synchronization (sync) is crucial to the network-level efficiency of cloud storage services, especially when handling large files with small increments. Practical delta sync techniques are, however, only available for PC clients and mobile apps, but not web browsers—the most pervasive and OS-independent access method. To bridge this gap, prior work concentrates on either reversing the delta sync protocol or utilizing the native client, all striving around the tradeoffs among efficiency, applicability, and usability and thus forming an “impossible triangle.” Recently, we note the advent of WebAssembly (WASM) , a portable binary instruction format that is efficient in both encoding size and load time. In principle, the unique advantages of WASM can make web-based applications enjoy near-native runtime speed without significant cloud-side or client-side changes. Thus, we implement a straightforward WASM-based delta sync solution, WASMrsync, finding its quasi-asynchronous working manner and conventional In-situ Separate Memory Allocation greatly increase sync time and memory usage. To address them, we strategically devise sync-async code decoupling and streaming compilation, together with Informed In-place File Construction. The resulting solution, WASMrsync+, achieves comparable sync time as the state-of-the-art (most efficient) solution with nearly only half of memory usage, letting the “impossible triangle” reach a reconciliation.

Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of existing cellular infrastructures of base carriers, while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a two-year measurement study towards understanding various fundamental aspects of today's MVNO ecosystem, including its architecture, customers, performance, economics, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with one million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics, statistical modeling, and machine learning to address the MVNO's key concerns with regard to data usage prediction, data plan reselling, customer churn mitigation, and billing delay reduction. Our proposed techniques can help achieve higher revenues and improved services for commercial MVNOs.