Adaptive Dynamic Voltage Scaling Research Articles

A Threshold Speed Computation Algorithm in Adaptive DVS Scheduling

Performance boosting of modern computing systems is constrained by the chip/circuit power dissipation. Dynamic voltage scaling (DVS) has been applied for reducing the energy consumption by dynamically changing the supply voltage. One can apply an adaptive scheme by computing a threshold speed of the supplied voltage, and adopting greedy online DVS scheduling algorithm when the voltage exceeds the threshold while choosing a conservative speed on the contrary. This paper presents an algorithm to determine the threshold speed. The proposed algorithm allows to obtaining the threshold speed for the adaptive DVS scheduling algorithm more efficiently.

A Threshold Speed Computation Algorithm in Adaptive DVS Scheduling

Performance boosting of modern computing systems is constrained by the chip/circuit power dissipation. Dynamic voltage scaling (DVS) has been applied for reducing the energy consumption by dynamically changing the supply voltage. One can apply an adaptive scheme by computing a threshold speed of the supplied voltage, and adopting greedy online DVS scheduling algorithm when the voltage exceeds the threshold while choosing a conservative speed on the contrary. This paper presents an algorithm to determine the threshold speed. The proposed algorithm allows to obtaining the threshold speed for the adaptive DVS scheduling algorithm more efficiently.

QoS-Sensitive Dynamic Voltage Scaling Algorithm for Wireless Multimedia Services

The past decade has seen a surge of research activities in the fields of mobile computing and wireless communication. In particular, recent technological advances have made portable devices, such as PDA, laptops, and wireless modems to be very compact and affordable. To effectively operate portable devices, energy efficiency and Quality of Service (QoS) provisioning are two primary concerns. Dynamic Voltage Scaling (DVS) is a common method for energy conservation for portable devices. However, due to the amount of data that needs to be dynamically handled in varying time periods, it is difficult to apply conventional DVS techniques to QoS sensitive multimedia applications. In this paper, a new adaptive DVS algorithm is proposed for QoS assurance and energy efficiency. Based on the repeated learning model, the proposed algorithm dynamically schedules multimedia service requests to strike the appropriate performance balance between contradictory requirements. Experimental results clearly indicate the performance of the proposed algorithm over that of existing schemes.

Statistical Framework for Video Decoding Complexity Modeling and Prediction

Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time-feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding.