arXiv subjects

Abhilash Jindal

Publications and source records attributed to Abhilash Jindal.

4 recordsLinked to original sources

arXiv2026

Generalizing and accelerating consistency checking for non-transactional distributed storage systems

Linearizability checkers check if an operation history, observed by concurrent clients, is linearizable. They are used in testing distributed storage systems, and use the classic Wing-Gong (WG) linearizability checking algorithm. In this paper, we generalize the WG algorithm to make linearizability checkers more versatile: we can check other non-transactional consistency guarantees, like ordered sequential consistency provided by Zookeeper. Equipped with this generalization, we can also check for system-specific consistency guarantees that introduce additional ordering constraints over operations in a history, as per the system's specification. Our experiments with 8 distributed storage systems show that checking for system-specific consistency guarantees is easy to realize, reduces false negatives in testing, helps debug consistency violations, can be up to 370x faster, and can scale to more concurrent clients within the same checking time budget. We report 6 new consistency violation bugs, out of which 5 could not be found with existing consistency checkers.

cs.DC↗

arXiv2025

WhiteLie: A Robust System for Spoofing User Data in Android Platforms

Android employs a permission framework that empowers users to either accept or deny sharing their private data (for example, location) with an app. However, many apps tend to crash when they are denied permission, leaving users no choice but to allow access to their data in order to use the app. In this paper, we introduce a comprehensive and robust user data spoofing system, WhiteLie, that can spoof a variety of user data and feed it to target apps. Additionally, it detects privacy-violating behaviours, automatically responding by supplying spoofed data instead of the user's real data, without crashing or disrupting the apps. Unlike prior approaches, WhiteLie requires neither device rooting nor altering the app's binary, making it deployable on stock Android devices. Through experiments on more than 70 popular Android apps, we demonstrate that WhiteLie is able to deceive apps into accepting spoofed data without getting detected. Our evaluation further demonstrates that WhiteLie introduces negligible overhead in terms of battery usage, CPU consumption, and app execution latency. Our findings underscore the feasibility of implementing user-centric privacy-enhancing mechanisms within the existing Android ecosystem.

cs.CR↗

arXiv2024

Automated PMC-based Power Modeling Methodology for Modern Mobile GPUs

The rise of machine learning workload on smartphones has propelled GPUs into one of the most power-hungry components of modern smartphones and elevates the need for optimizing the GPU power draw by mobile apps. Optimizing the power consumption of mobile GPUs in turn requires accurate estimation of their power draw during app execution. In this paper, we observe that the prior-art, utilization-frequency based GPU models cannot capture the diverse micro-architectural usage of modern mobile GPUs.We show that these models suffer poor modeling accuracy under diverse GPU workload, and study whether performance monitoring counter (PMC)-based models recently proposed for desktop/server GPUs can be applied to accurately model mobile GPU power. Our study shows that the PMCs that come with dominating mobile GPUs used in modern smartphones are sufficient to model mobile GPU power, but exhibit multicollinearity if used altogether. We present APGPM, the mobile GPU power modeling methodology that automatically selects an optimal set of PMCs that maximizes the GPU power model accuracy. Evaluation on two representative mobile GPUs shows that APGPM-generated GPU power models reduce the MAPE modeling error of prior-art by 1.95x to 2.66x (i.e., by 11.3% to 15.4%) while using only 4.66% to 20.41% of the total number of available PMCs.

cs.PF↗

arXiv2022

An Empirical Study on the Impact of Deep Parameters on Mobile App Energy Usage

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these "deep" parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app's energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps.

cs.SE↗