Resource Provisioning and Profit Maximization for Transcoding in Information Centric Networking
This work addresses the challenge for transcoding service providers in efficiently managing computing resources to maximize profit, representing an incremental improvement in cloud video transcoding systems.
The paper tackles the problem of resource provisioning and profit maximization for video transcoding in adaptive bitrate streaming by designing a cloud-based system that jointly optimizes task scheduling and resource allocation, resulting in reduced resource consumption and higher profit compared to baselines.
Adaptive bitrate streaming (ABR) has been widely adopted to support video streaming services over heterogeneous devices and varying network conditions. With ABR, each video content is transcoded into multiple representations in different bitrates and resolutions. However, video transcoding is computing intensive, which requires the transcoding service providers to deploy a large number of servers for transcoding the video contents published by the content producers. As such, a natural question for the transcoding service provider is how to provision the computing resource for transcoding the video contents while maximizing service profit. To address this problem, we design a cloud video transcoding system by taking the advantage of cloud computing technology to elastically allocate computing resource. We propose a method for jointly considering the task scheduling and resource provisioning problem in two timescales, and formulate the service profit maximization as a two-timescale stochastic optimization problem. We derive some approximate policies for the task scheduling and resource provisioning. Based on our proposed methods, we implement our open source cloud video transcoding system Morph and evaluate its performance in a real environment. The experiment results demonstrate that our proposed method can reduce the resource consumption and achieve a higher profit compared with the baseline schemes.