Data packet scheduling method for performing combined optimization on energy consumption and video quality under cloud computation

Abstract

The invention discloses a data packet scheduling method for performing combined optimization on energy consumption and video quality under cloud computation. The method comprises the following steps: (1) assuming a cloud computation video scheduling system model and defining utility function of video distortion reduction quantity and energy consumption, collecting user requests by using a cloud data center of the model, transmitting request information to a video scheduler by users, and performing video scheduling optimization on a user request queue by using the video scheduler according to the user requests; (2) describing dependence relationship between frames of schedulable frame sets based on the video data by using a DAG model; and (3) fusing the dependence relationship between the frames of each frame set and system state optimal scheduling priority at each time slot, and transmitting video data packets to the users by using the video scheduler according to the optimal scheduling sequence of data packets of the current frame set. By the method, energy consumption of a server can be reduced and the quality of video received by the users can be improved, so that utility maximization of video scheduling system target time in cloud computation can be realized.

Description

technical field [0001] The invention relates to a data packet scheduling method for joint optimization of energy consumption and video quality under cloud computing. Background technique [0002] Cloud computing refers to the distribution of computing tasks on a resource pool composed of a large number of computer systems, so that various application systems can obtain computing power, storage space and software information services as needed. From a computing point of view, cloud computing is a large-scale distributed computing model, which is a networked data center that includes A server with high-performance computing capabilities, low cost, and dynamic scalability makes it an important development direction for future computing. [0003] With the rapid development of communication technology, it has become possible for people to obtain video data in real time through the communication network. The characteristics of video data are: large data volume, high calculation ...

AI Technical Summary

Problems solved by technology

In the traditional data packet scheduling methods, most of the dependencies between different frames of the video are ignored, often based on the traditional distribution network, and the load of the server is not fully considered in terms of energy consumption. In cloud computing, scheduling between computer systems The energy consumption brought by the server is closely related to the load and frequency of the server, so these traditional packet scheduling methods are not suitable for cloud computing environments

In addition, the traditional data packet scheduling method in video transmission, in the process of data packet scheduling, only considers the target optimization at the current moment, and does not consider the target optimization at the target time

[0005]Nicholas Mastronarde et al. proposed a point-to-point video packet scheduling method with minimum energy consumption in a fading channel, and adopted a Markov decision process to control the scheduling process model, however, in this scheduling process, this method does not consider the dependencies between different frames of the video

Aditya Dua et al. proposed a channel, deadline and distortion-aware packet scheduling method, which only schedules one video frame in each time slot, ignores the dependencies between different frames of the video, and does not consider the server-side energy consumption

Fangwen Fu et al. proposed a video packet scheduling method under wireless time-varying channels. This method considers the dependency between different frames of the video and the energy consumption of the sending end, and uses the Markov decision process to schedule However, this method does not consider the load of the sending server in terms of energy consumption, so it is not suitable for the energy consumption optimization of servers in cloud computing

Images