TLP-aware power management for interactive mobile applications

Abstract

As smartphones proliferate, understanding the power behaviors of smartphones and their applications has become critical to overcome their battery limitation. However, the applications running on the smartphones are commonly sensitive to user interaction, and their behaviors are different from traditional CPU benchmark applications. This paper characterizes the thread-level parallelism (TLP) and CPU usage patterns of interactive smartphone applications. The paper presents how applications exhibit different types of user interactions and how differently various interaction phases provide TLP. Based on the analysis on the TLP and interaction phases of mobile applications, we propose an improved power management scheme using the dynamic voltage and frequency scaling mechanism available in mobile platforms. The proposed improvement uses a scheduling scheme, which identifies and prioritizes a dominant thread, which determines the perceived performance of mobile applications. Using a utility-based power model, the scheme finds the best number of active cores for energy efficiency, and either pack or unpack non-dominant threads to the most energy efficient number of active cores. In addition to the TLP-oriented scheduling component, the scheme also controls the headroom for frequency scaling dynamically, to adapt to the user interaction phases. While maintaining the unnecessary headroom to the minimum, the proposed scheme improves the energy efficiency without any perceived performance degradation.