A scalable load generation framework for evaluation of video streaming workflows in the cloud

May 2020
Roberto Ramos-Chavez, Theo Karagkioules, Rufael Mekuria
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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.

Publication: MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference