1Multimedia Processing Laboratory, School of Electrical and Computer Engineering, University of Tehran
2School of Electrical and Computer Engineering Multimedia Processing Laboratory University of Tehran, Iran
3Multimedia Processing Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran
In recent years, cloud computing has emerged as a viable alternative for many computationally intensive applications. Offloading an application to the cloud has many advantages, but power consumption is still an important concern. Service providers should minimize power while maintaining customer’s quality of service requirements. Dynamic Voltage and Frequency Scaling (DVFS) is an effective method to optimize power consumption by minimizing the processor speed to a level that satisfies the application constraints. In applications such as real-time video encoding, where computational complexity depends on the video content, it is important to be able to adjust voltage and frequency dynamically. Incorrect DVFS may cause over-provisioning of resources or missing application deadlines. In other words, one of the key challenges of DVFS is the accuracy of workload estimation. In this paper, we propose a workload estimation method using low level Hardware Performance Counters (HPCs) for content dependent multimedia applications. The proposed technique has been used to estimate the workload of an H.264/AVC video encoder at the Group-Of-Pictures (GOP) level. Simulation results indicate that 23% energy saving can be achieved in comparison with ondemand frequency setting policy used in Linux.
Workload Prediction, Power Consumption, DVFS, Hardware Performance counters