User-centric Joint Power and Thermal Management for Smartphones

Abstract

Excessive power consumption and high temperatures are some of the key factors limiting battery charge life and performance of current mobile devices or smartphones. Existing power management solutions do not account for user-experience, which can impact user satisfaction. In this paper we propose a joint power and thermal management solution, which takes a proactive approach in reducing energy consumption while providing expected user-experience. The proposed technique modulates the operating conditions based on users application preferences and exploits the "change blindness" effect to reduce display power consumption. Another important aspect of our implementation is that it does not require any restructuring of the underlying operating system. A novel thermal model of the entire smartphone was built with the purpose of monitoring and controlling the operating conditions to keep the device temperatures within safe operating ranges. Our ready-to-use management technique has been implemented on Google Nexus 5 and has been demonstrated to achieve a 46% application-specific savings on power consumption and up to 35% savings in power consumption at the device level. The mean temperature estimation error is 1.17 • C.

Optimal Planning of Dynamic Thermal Management for NANS (N-App N-Screen) Services

“…As a compromise between these two groups of studies, Mercati et al [16] proposed a simplified CTM-based thermal prediction method using only observable operating parameters instead of actual power measurements or power models. It provides good prediction accuracy and is practically applicable since it uses only observable operating parameters of hardware components that have neither power sensors nor power models.…”

“…Our proposed thermal prediction method is based on Mercati et al's method [16]. Additionally, we consider display chipsets as major heat sources used for NANS technology.…”

“…To provide better QoS, several studies presented proactive DTM techniques which consider QoS characteristics of applications. Mercati et al [16] presented a proactive DTM technique which minimizes QoS loss while preventing thermal violations. Also, Prakash et al [29] proposed a proactive DTM technique which reduces the temperature variance while achieving high QoS for running applications.…”

“…To reduce the effects of noise, we also use the average value of the temperatures collected for 1 s. Using such made data acquisition system, we now can measure T Miracast (t) and T HDMI (t). For the second issue, we use the following claim made by Mercati et al [16]: Claim 1. For a hardware component, its operating parameter, i.e., the operating frequency for a CPU core and bit-rate for a display chipset, has a linear relationship with its power consumption without significant loss in accuracy.…”

“…To protect microprocessors from thermal violations, there have been many studies on thermal prediction-based DTM (Dynamic Thermal Management) [6][7][8][9][10][11][12][13][14][15][16] to predict the temperature and then to lower the resource usage if the predicted temperature is at risk of exceeding the thermal threshold. Since those studies consider rapid temperature increases as abnormal and rare cases, they use simple solutions despite the risk of thermal violations.…”