Bartek Wydrowski, Google Research; Robert Kleinberg, Google Research and Cornell; Stephen M. Rumble, Google (YouTube); Aaron Archer, Google Research
We provide PReQuaL (Probing to Reduce Queuing and Latency)a load balancer for dispersed multi-tenant systems. PReQuaL is developed to reduce real-time demand latency in the existence of heterogeneous server capabilities and non-uniform, time-varying villain load. To accomplish this, PReQuaL actively probes server load and leverages the power of d options paradigmextending it with asynchronous and multiple-use probes. Cutting versus gotten knowledge, PReQuaL does not stabilize CPU load, however rather chooses servers according to approximated latency and active requests-in-flight (RIF). We check out the significant style functions of PReQuaL on a testbed system and explain our experience utilizing it to stabilize load within YouTube, where it has actually been running for more than a year. PReQuaL has actually drastically reduced tail latency, mistake rates, and resource usage, allowing YouTube and other production systems at Google to perform at much greater usage.
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