Online tuning of Dynamic Power Management for efficient execution of interactive workloads

Abstract

Abstract-Modern mobile devices contain powerful MultiProcessor System-on-Chips (MPSoCs) that are performance throttled by Dynamic Power Management (DPM) runtime systems to extend battery lifetime. Applications on mobile devices commonly generate highly interactive workloads, dependent on interaction between the processor cores, peripherals, external resources and the user, such as touch input during web-browsing. Inevitably, a subset of interactive workloads are affected by delays caused by data unavailability, e.g. loss or delay of data packets during voice-over-IP. At the same time, the system is required to respond quickly upon data retrieval to ensure that the user Quality of Experience (QoE) metrics (frame-rate, latency, etc.) are not degraded. Traditionally, operating systems have mitigated this problem with periodic sampling or event-driven approaches. Through experimentation using a mobile MPSoC platform, however, we demonstrate that improving the tuning of DPM parameters for certain interactive user inputs can provide energy savings of up to 21% or QoE improvements of up to 36%, when compared with the traditional approach. To capture these improvements, we propose a dynamic modeling of user input and data resource access times (e.g. mobile network bandwidth and latency) for interactive workloads, which is based on workload profiling and which we refer to herein as inelasticity analysis. The proposed approach is implemented through online tuning of a DPM runtime in the Android operating system and is validated through a Monte Carlo simulation of interactive workloads. In comparison to the default DPM tuning, the proposed approach achieves energy savings of 13% or QoE improvement of 27% or a selectable trade-off, e.g. 9% energy savings and 15% QoE improvement.

Mitigating Interactive Performance Degradation From Mobile Device Thermal Throttling

“…A characteristic common to all of the interactive workloads used in the previous experiment is that high performance is required for short sporadic periods of time. This may be a response to user input or the conclusion of a delay waiting for external resources such as network access [21]. For the remainder of time, the performance required is lower and unlikely to be at a level that would be affected by a frequency cap.…”

A survey of techniques for improving efficiency of mobile web browsing

“…Objective Energy 3,10,13,14,16,17,24,27,30,[60][61][62][63] Webpage loading time 3,[8][9][10][11][12][13][14]16,17,[24][25][26][27][28][29][30][31]33,34,36,38,40,[43][44][45][46][47][48][49][50]51,52,[56][57][58]61,[64][65][66]…”