Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

Abstract

High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (i.e., transcoding) video streams to match the characteristics of viewers' devices (e.g., 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.

Improving Robustness of Heterogeneous Serverless Computing Systems via Probabilistic Task Pruning

“…in HC systems, namely uncertainty in tasks' execution times and uncertainty in tasks' arrival rate [17], [18].…”

“…Users issue independent service requests (termed tasks) from a set of offered service types (termed task-types). A task in our study is modeled as an independent video segment in the form of Group Of Pictures (GOPs) that is sequentially processed (e.g., transcoded [17]) within a deadline constraint. Each task has an individual hard deadline, which is the presentation time of that video segment [15], [25].…”

F-FDN: Federation of Fog Computing Systems for Low Latency Video Streaming

“…This distribution is obtained from historical execution times of a particular processing type (e.g., bit-rate transcoding) for a segment. It has been shown that the processing time of a video segment exhibits a normal distribution [8]. Let N E i (µ i j , σ i j ) be the probability distribution of completing the processing of segment i on FDN j; also let N T i (µ jv , σ jv ) be a normal distribution representing latency to deliver segment i from the local FDN j to the viewer's device v. Then, the probability distribution of delivering segment i to the viewer is calculated by convolving the two distributions as shown in Equation 2.…”

“…In addition, caching on CDNs is less effective because of the fact that streaming providers have to maintain multiple versions of the same video to be able to support heterogeneous display devices and network conditions [6]. As such, instead of preprocessing video streams into multiple versions, mechanisms for on-demand processing (e.g., on-demand transcoding [6]) of video streams is becoming prevalent [7], [8]. However, the challenge is that on-demand video processing cannot be performed on CDNs since they are predominantly used for caching purposes [9].…”

“…Video streaming is achieved via processing and streaming independent video segments in the form of Group Of Pictures (GOP) [12]. A video stream generally consists of multiple GOPs, depending on the content type and length (i.e., timespan) of the video [8].…”