Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

TL;DR

This paper thoroughly analyzes the performance of video transcoding on heterogeneous cloud VMs and proposes a model to optimize resource utilization considering performance and cost trade-offs.

Contribution

It provides the first detailed performance analysis of video transcoding on heterogeneous cloud VMs and introduces a model for selecting suitable VMs based on performance and cost.

Findings

Performance varies significantly across different VM types.

The proposed model accurately predicts transcoding times and costs.

Resource provisioning can be optimized using the model for better efficiency.

Abstract

High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (ie, transcoding) video streams to match the characteristics of viewers' devices (eg, in terms of spatial resolution or supported formats). Considering the computational cost of the transcoding operation and the surge in video streaming demands, Streaming Service Providers (SSPs) are becoming reliant on cloud services to guarantee Quality of Service (QoS) of streaming for their viewers. Cloud providers offer heterogeneous computational services in form of different types of Virtual Machines (VMs) with diverse prices. Effective utilization of cloud services for video transcoding requires detailed performance analysis of different video transcoding operations on the heterogeneous cloud VMs. In this research, for the first time, we provide a thorough analysis of the performance of the video stream transcoding on heterogeneous cloud VMs. Providing such analysis is crucial for efficient prediction of transcoding time on heterogeneous VMs and for the functionality of any scheduling methods tailored for video transcoding. Based upon the findings of this analysis and by considering the cost difference of heterogeneous cloud VMs, in this research, we also provide a model to quantify the degree of suitability of each cloud VM type for various transcoding tasks. The provided model can supply resource (VM) provisioning methods with accurate performance and cost trade-offs to efficiently utilize cloud services for video streaming.