Whom to Blame? Automatic Diagnosis of Performance Bottlenecks on Smartphones

The past decade has witnessed a tremendous growth in the variety and complexity of mobile applications (apps). Although considerable amount of efforts have been spent to improve app performance, smartphones nowadays still face many performance challenges. We discover that the resource contention of multiple running apps, caused by resource bottleneck(s), is a key factor that affects the smartphone performance. In this paper, we present APB, an A utomatic tool that detects Performance issues caused by resource B ottleneck(s) on commodity Android smartphones. APB employs an innovative bottleneck-hypersurface model to quantify performance issues given a specific system state. Then, based on the model, APB identifies a list of apps that contribute most to the resource contention, which can well inform the end user to take action such as killing background apps to resolve the performance issue. We implement APB on commodity Android platforms and widely evaluate its effectiveness with real user studies. Results show that APB outperforms three baseline approaches and helps users to improve smartphone performance by 10 to 67 percent, with less than 1 percent runtime overhead.