Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services

TL;DR

This paper introduces CVS2, a cloud-based architecture for on-demand video transcoding that balances cost-efficiency and robust quality of service by dynamically managing heterogeneous virtual machines based on workload affinity.

Contribution

It proposes a novel QoS-aware scheduling and self-configurable VM provisioning approach for cost-effective, on-demand video transcoding in cloud environments.

Findings

Reduces transcoding costs by up to 85%.

Maintains robust QoS under diverse workloads.

Dynamically reconfigures VM clusters for workload affinity.

Abstract

Video streams usually have to be transcoded to match the characteristics of viewers' devices. Streaming providers have to store numerous transcoded versions of a given video to serve various display devices. Given the fact that viewers' access pattern to video streams follows a long tail distribution, for the video streams with low access rate, we propose to transcode them in an on-demand manner using cloud computing services. The challenge in utilizing cloud services for on-demand video transcoding is to maintain a robust QoS for viewers and cost-efficiency for streaming service providers. To address this challenge, we present the Cloud-based Video Streaming Services (CVS2) architecture. It includes a QoS-aware scheduling that maps transcoding tasks to the VMs by considering the affinity of the transcoding tasks with the allocated heterogeneous VMs. To maintain robustness in the presence of varying streaming requests, the architecture includes a cost-efficient VM Provisioner. This component provides a self- configurable cluster of heterogeneous VMs. The cluster is reconfigured dynamically to maintain the maximum affinity with the arriving workload. Results obtained under diverse workload conditions demonstrate that CVS2 architecture can maintain a robust QoS for viewers while reducing the incurred cost of the streaming service provider up to 85%