Serverless computing has become a promising paradigm for video processing workflows, offering simplified deployment and flexible management of business logic. However, the dynamic, multi-stage nature of video processing pipelines poses significant challenges for traditional serverless resource management, particularly in efficiently modeling optimal configurations and adapting to rapidly evolving pipeline structures. To address this challenge, we propose ConfigNavigator, a video pipeline resource tuning framework capable of adapting to dynamic inputs and pipeline structures with minimal overhead. In the offline phase, ConfigNavigator models function execution time distributions at the fundamental operation level and leverages graph theory to decompose complex video processing pipelines, thereby obtaining optimal configurations with minimal overhead. In the online phase, it dynamically adjusts function configurations on critical paths through real-time performance feedback, ensuring pipeline performance stability across varying workloads. We evaluate ConfigNavigator using real video streams on the commercial serverless platform AWS Lambda. Compared to state-of-the-art baselines, ConfigNavigator reduces configuration search time by 94.11% while decreasing end-to-end pipeline processing time by 13.97%.

With recent advances on cellular technologies pushing the boundary of cellular performance, cellular reliability has become a key concern of their adoption and deployment. To fully understand cellular reliability, we work with a major Android phone vendor, Xiaomi, to conduct a long-term (2020-2024) and large-scale (involving 123M users) measurement study in China, with coarse-grained general statistics and fine-grained sampling diagnostics. Our measurement reveals contrasting evolution trends of cellular failures in different stages of the data connection: in the past five years, failures after connection establishment decrease remarkably (by 29%), while failures during connection setup exhibit a sharp increase (by 38%). Our analysis illustrates that the contrast stems from the joint impact of multiple stakeholders, including ISPs’ increasing deployment of 5G base stations, 5G infrastructure upgrade from NSA (Non-Standalone) to SA (Standalone) mode, software defects coming from Android’s adaptation to new cellular technologies, and so forth. Our work provides actionable insights for improving cellular reliability at scale. More importantly, we have built on our insights to develop enhancements that effectively address cellular reliability issues with remarkable real-world impact—our optimizations have reduced 38% cellular connection failures for 5G phones and 31% failure recovery time across all phones.

Android system has been widely deployed in energy-constrained IoT devices for many practical applications, such as smart phone, smart home, healthcare, fitness, and beacons. However, Android users oftentimes suffer from app crashes, which directly disrupt user experience and could lead to data loss. Till now, the community have limited understanding of their prevalence, characteristics, and root causes. In this article, we make an in-depth study of the crash events regarding ten very popular apps of different genres, based on fine-grained system-level traces crowd-sourced from 93 million Android devices. We find that app crashes occur prevalently on the various hardware models studied, and better hardware does not seem to essentially relieve the problem. Most importantly, we unravel multi-fold root causes of app crashes, and pinpoint that the most crashes stem from the subtle yet crucial inconsistency between app developers’ supposed memory/process management model and Android’s actual implementations. We design practical approaches to addressing the inconsistency; after large-scale deployment, they reduce 40.4% of the app crashes with negligible system overhead. In addition, we summarize important lessons learned from this study, and have released our measurement code/data to the community.

Virtual devices based on device emulation have been widely used in lab research of mobile app testing for their efficiency and low cost. However, it remains controversial to use virtual devices for app testing in industry, given the inherent difficulties of high-fidelity emulation across diverse mobile systems and devices. Hence, mobile app companies still rely on physical device farms or services like AWS Device Farm.

Androidoverlayenables one app to draw over other apps by creating an extraViewlayer atop the hostView, which nevertheless can be exploited by malicious apps (malware) to attack users. To combat this threat, prior countermeasures concentrate on restricting the capabilities of overlays at the OS level while sacrificing overlays’ usability; recently, the overlay mechanism has been substantially updated to prevent a variety of attacks, which however can still be evaded by considerable adversaries. To address these shortcomings, a more pragmatic approach is to enableearly detectionof overlay-based malware during the app market review process, so that all the capabilities of overlays can stay unchanged. For this purpose, in this paper we first conduct a large-scale comparative study of overlay characteristics in benign and malicious apps, and then implement the OverlayChecker system to automatically detect overlay-based malware for one of the world’s largest Android app stores. In particular, we have made systematic efforts in feature engineering, UI exploration, emulation architecture, and run-time environment, thus maintaining high detection accuracy (97 percent precision and 97 percent recall) and short per-app scan time ($\sim$1.7 minutes) with only two commodity servers, under an intensive workload of$\sim$10K newly submitted apps per day.