Tetris: Efficient Intra-Datacenter Calls Packing for Large Conferencing Services

TL;DR

This paper introduces Tetris, a framework for optimizing call packing in datacenter servers to reduce CPU hotspots, improve performance, and lower costs in large conferencing services.

Contribution

Tetris is a novel multi-step framework that uses historical data and linear optimization to balance call loads across MPs, addressing variability and burstiness in call arrivals.

Findings

Reduces hot MP CPU utilization by at least 2.5X

Improves call packing efficiency in large datacenter traces

Decreases hosting costs and performance degradation risks

Abstract

Conference services like Zoom, Microsoft Teams, and Google Meet facilitate millions of daily calls, yet ensuring high performance at low costs remains a significant challenge. This paper revisits the problem of packing calls across Media Processor (MP) servers that host the calls within individual datacenters (DCs). We show that the algorithm used in Teams -- a large scale conferencing service as well as other state-of-art algorithms are prone to placing calls resulting in some of the MPs becoming hot (high CPU utilization) that leads to degraded performance and/or elevated hosting costs. The problem arises from disregarding the variability in CPU usage among calls, influenced by differences in participant numbers and media types (audio/video), compounded by bursty call arrivals. To tackle this, we propose Tetris, a multi-step framework which (a) optimizes initial call assignments by leveraging historical data and (b) periodically migrates calls from hot MPs using linear optimization, aiming to minimize hot MP usage. Evaluation based on a 24-hour trace of over 10 million calls in one DC shows that Tetris reduces participant numbers on hot MPs by at least 2.5X.