TY - GEN
T1 - Energy-Aware CPU Frequency Scaling for Mobile Video Streaming
AU - Hu, Wenjie
AU - Cao, Guohong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/13
Y1 - 2017/7/13
N2 - The energy consumed by video streaming includes the energy consumed for data transmission and CPU processing, which are both affected by the CPU frequency. High CPU frequency can reduce the data transmission time but it consumes more CPU energy. Low CPU frequency reduces the CPU energy but increases the data transmission time and then increases the energy consumption. In this paper, we aim to reduce the total energy of mobile video streaming by adaptively adjusting the CPU frequency. Based on real measurement results, we model the effects of CPU frequency on TCP throughput and system power. Based on these models, we propose an Energy-aware CPU Frequency Scaling (EFS) algorithm which selects the CPU frequency that can achieve a balance between saving the data transmission energy and CPU energy. Since the downloading schedule of existing video streaming apps is not optimized in terms of energy, we also propose a method to determine when and how much data to download. Through trace-driven simulations and real measurement, we demonstrate that the EFS algorithm can reduce 30% of energy for the Youtube app, and the combination of our download method and EFS algorithm can save 50% of energy than the default Youtube app.
AB - The energy consumed by video streaming includes the energy consumed for data transmission and CPU processing, which are both affected by the CPU frequency. High CPU frequency can reduce the data transmission time but it consumes more CPU energy. Low CPU frequency reduces the CPU energy but increases the data transmission time and then increases the energy consumption. In this paper, we aim to reduce the total energy of mobile video streaming by adaptively adjusting the CPU frequency. Based on real measurement results, we model the effects of CPU frequency on TCP throughput and system power. Based on these models, we propose an Energy-aware CPU Frequency Scaling (EFS) algorithm which selects the CPU frequency that can achieve a balance between saving the data transmission energy and CPU energy. Since the downloading schedule of existing video streaming apps is not optimized in terms of energy, we also propose a method to determine when and how much data to download. Through trace-driven simulations and real measurement, we demonstrate that the EFS algorithm can reduce 30% of energy for the Youtube app, and the combination of our download method and EFS algorithm can save 50% of energy than the default Youtube app.
UR - http://www.scopus.com/inward/record.url?scp=85027246262&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027246262&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2017.74
DO - 10.1109/ICDCS.2017.74
M3 - Conference contribution
AN - SCOPUS:85027246262
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 2314
EP - 2321
BT - Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
A2 - Lee, Kisung
A2 - Liu, Ling
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
Y2 - 5 June 2017 through 8 June 2017
ER -
