.png){kind=icon}
返回
A Workload-Aware DVFS Robust to Concurrent Tasks for Mobile Devices
.png){kind=icon}
PRE
AI
DOI：10.1145/3570361.3592524.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Power governing is a critical component of modern mobile devices, reducing heat generation and extending device battery life. A popular technology of power governing is dynamic voltage and frequency scaling (DVFS), which adjusts the operating frequency of a processor to balance its performance and energy consumption. With the emergence of diverse workloads on mobile devices, traditional DVFS methods that do not consider workload characteristics become suboptimal. Recent application-oriented methods propose dedicated and effective DVFS governors for individual application tasks. Since their approach is only tailored to the targeted task, performance drops significantly when other tasks run concurrently, which is however common on today's mobile devices. In this paper, our key insight is that hardware meta-data, widely used in existing DVFS designs, has great potential to enable capable workload awareness and task concurrency adaptability for DVFS, but they are underexplored. We find that workload characteristics can be described in a hyperspace composed of multiple dimensions derived from these meta-data to form a novel workload contextual indicator to profile task dynamics and concurrency. On this basis, we propose a meta-state metric to capture this relationship and design a new solution, GearDVFS. We evaluate it for a rich set of application tasks, and it outperforms state-of-the-art methods.
Keyword:
Mobile Devices
DVFS
Power Management
期刊
机构
引用论文
A general model for the propagation of uncertainty in measurements into heat transfer simulations and its application to cryosurgery.png)
Cryobiology
CryobiologyIF0
PREAI
分享收藏Template Synthesis of Nanowires in Porous Polycarbonate Membranes: Electrochemistry and Morphology
PREAI
分享收藏Hybrid sensorless control of IPMSM Combining high frequency injection method and back EMF method
PREAI
分享收藏
查看更多内容