Paper Info
Title
A scalable load generation framework for evaluation of video streaming workflows in the cloud
Abstract
HTTP Adaptive Streaming (HAS) is increasingly deployed at large, gradually replacing traditional broadcast. However, testing large-scale deployments remains challenging, costly and error-prone. Especially, testing with realistic streaming loads from massive numbers of users is challenging and costly. To improve this, we introduce an open-source load testing tool that can be deployed in the cloud or on-premise in a distributed manner, for load generation. Our presented tool is an extension of an existing open-source web-application load-testing tool. In particular we have added functionality, that includes streaming load generation for a multitude of protocols (i.e. Dynamic Adaptive Streaming over HTTP (DASH) and HTTP-Live-Streaming (HLS)) and use-case implementations (e.g. live streaming, Video on Demand (VoD), bit-rate switching). The extension facilitates testing streaming back-ends at scale in a resource-efficient manner. We illustrate our tool's capabilities via a series of use-cases, designed to test, among others, how streaming deployments perform under different load scenarios, i.e. steep or gradual user ramp-up and stability testing over long periods.
Year
DOI
Venue
2020
10.1145/3339825.3394930
MMSys '20: 11th ACM Multimedia Systems Conference Istanbul Turkey June, 2020
Keywords
DocType
ISBN
Performance testing, Cloud computing, Experimentation
Conference
978-1-4503-6845-2
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Roberto Ramos-Chavez100.34
Theo Karagkioules210.68
Rufael Mekuria317814.61