Heterogeneous cloud resource allocation: a case study on real-time transcoding in live streaming

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications Pub Date : 2025-09-18 DOI:10.1016/j.eswa.2025.129700

Yinuo Li , Jin-Kao Hao , Kwong Meng Teo , Liwei Song

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引用次数: 0

Abstract

The explosion in popularity of crowdsourced live streaming (CLS) has led to a huge increase in demand for cloud resources to support real-time video transcoding. CLS transcoding is real-time, geographically distributed and computationally intensive. Therefore, transcoding service providers need to cost-effectively utilize diverse heterogeneous cloud resources, while guaranteeing quality of service standards to ensure a good streaming experience for the viewers. To support the above, we developed a novel proactive-reactive resource allocation framework that optimizes the overall cost of supporting the CLS transcoding service using heterogeneous edge and cloud computing resources. The offline proactive policy evaluator aims to provide a good and adaptable resource usage plan in advance, matching the predicted demand with the heterogeneous resources. The reactive execution module monitors the actual demand online and controls the resource usage to compensate for deviations from the offline prediction. Our experiments show that the proposed approach leads to a cost reduction of 42 % compared to the fixed usage ratio strategy based on expert knowledge.

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异构云资源分配：实时流媒体中的实时转码案例研究

众包直播（CLS）的爆炸式增长导致对云资源的需求大幅增加，以支持实时视频转码。CLS转码是实时的、地理分布的和计算密集型的。因此，转码服务商需要在保证服务质量标准的前提下，经济高效地利用多种异构云资源，为观众提供良好的流媒体体验。为了支持上述功能，我们开发了一种新颖的主动响应资源分配框架，该框架优化了使用异构边缘和云计算资源支持CLS转码服务的总体成本。离线主动策略评估器旨在提前提供良好且适应性强的资源使用计划，将预测的需求与异构资源相匹配。响应式执行模块在线监视实际需求并控制资源使用，以补偿与离线预测的偏差。我们的实验表明，与基于专家知识的固定使用率策略相比，该方法的成本降低了42%。

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来源期刊

Expert Systems with Applications 工程技术-工程：电子与电气

CiteScore

13.80

自引率

10.60%

发文量

2045

审稿时长

8.7 months

期刊介绍： Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.