Joint optimization of DVFS and low-power sleep-state selection for mobile platforms

To provide the ultimate mobile user experience, extended battery life is critical to small form-factor mobile platforms such as smartphones and tablets. Dynamic voltage and frequency scaling (DVFS) and low-power CPU/platform sleep states are commonly used power management features, as they allow dyn...

Full description

Saved in:
Bibliographic Details
Main Authors: Min, Alexander W., Ren Wang, Tsai, James, Tai, Tsung-Yuan Charlie
Format: Conference Proceeding
Language:English
Subjects:
dynamic voltage and frequency scaling (DVFS)
Energy consumption
Energy efficiency
Mobile communication
mobile platform
Optimized production technology
Power demand
sleep states
Streaming media
Time-frequency analysis
workload characteristics
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To provide the ultimate mobile user experience, extended battery life is critical to small form-factor mobile platforms such as smartphones and tablets. Dynamic voltage and frequency scaling (DVFS) and low-power CPU/platform sleep states are commonly used power management features, as they allow dynamic control of power and performance to the time-varying needs of workloads. Despite the potential power saving benefit from synergistic integration of DVFS and sleep-state selection, it is challenging to optimize them jointly for mobile workloads (e.g., video streaming), and most existing work considers them only individually. To address this problem, we study joint optimization of CPU frequency (a.k.a. CPU P-states) and CPU/platform sleep-state selections to reduce energy consumption in mobile platforms. This joint optimization becomes feasible with advanced power management techniques and power aware software development methodologies that regulate (e.g., coalesce/align) system activities, making workload characteristics and system idle duration more deterministic and predictable. We then analyze the optimal operating state that minimizes the expected platform energy consumption based on workload characteristics, and present an algorithm to adapt to it at run time. Our evaluation results on mobile workloads show that the proposed scheme can reduce system power consumption by up to 24%, compared to the conventional CPU-utilization-based approach, which seeks mainly to minimize processor energy.
ISSN:1550-3607
1938-1883
DOI:10.1109/ICC.2014.6883870