Media streams allocation and load patterns for a WebRTC cloud architecture

Web Real-Time Communication Web Real-Time Communication (WebRTC) is seeing a rapid rise in adoption footprint. This standard provides an audio/video platform-agnostic communications framework for the Web build-in right in the browser. The complex technology stack of a full implementation of the standard is vast and includes elements of various computational disciplines like: content delivery, audio/video processing, media transport and quality of experience control, for both P2P and Cloud relayed communications. To the best of our knowledge, no previous study examines the impact of Cloud back-end load and media quality at production scale for a media stream processing application, as well as load mitigation for Cloud media Selective Forwarding Units. The contribution of this work is the analysis and exploitation of server workload (predictable session size, strong periodical load patterns) and media bit rate patterns that are derived from real user traffic (toward our test environment), over an extended period of time. Additionally, a simple and effective load balancing scheme is discussed to fairly distribute big sessions over multiple servers by exploiting the discovered patterns of stable session sizes and server load predictability. A Cloud simulation environment was built to compare the performance of the algorithm with other load allocation policies. This work is a basis for more advanced resource allocation algorithms and media Service Level Objectives (SLO) spanning multiple Cloud entities.