2 research outputs found
Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services
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.Comment: 15 page
Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services
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%Comment: IEEE Transactions on Parallel and Distributed System