To Auto Scale or Not to Auto Scale
Nathan D. Mickulicz, Priya Lakshmi Narasimhan, Rajeev Gandhi · International Conference on Autonomic Computing · 2013
YinzCam is a cloud-hosted service that provides sports fans with real-time scores, news, photos, statistics, live radio, streaming video, etc., on their mobile devices. YinzCam’s infrastructure is currently hosted on Amazon Web Services (AWS) and supports over 7 million downloads of the official mobile apps of 40+ professional sports teams and venues. YinzCam’s workload is necessarily multi-modal (e.g., pre-game, in-game, postgame, game-day, non-gameday, in-season, off-season) and exhibits large traffic spikes due to extensive usage by sports fans during the actual hours of a game, with normal game-time traffic being twenty-fold of that on nongame days. We discuss the system’s performance in the three phases of its evolution: (i) when we initially deployed the YinzCam infrastructure and our users experienced unpredictable latencies and a large number of errors, (ii) when we enabled AWS’ Auto Scaling capability to reduce the latency and the number of errors, and (iii) when we analyzed the YinzCam architecture and discovered opportunities for architectural optimization that allowed us to provide predictable performance with lower latency, a lower number of errors, and at lower cost, when compared with enabling Auto Scaling.