Zoolander: Efficiently Meeting Very Strict, Low-Latency SLOs.

Internet services access networked storage many times while processing a request. Just a few slow storage accesses per request can raise response times a lot, making the whole service less usable and hurting profits. This paper presents Zoolander, a key value store that meets strict, low latency service level objectives (SLOs). Zoolander scales out using replication for predictability, an old but seldom-used approach that uses redundant accesses to mask outlier response times. Zoolander also scales out using traditional replication and partitioning. It uses an analytic model to efficiently combine these competing approaches based on systems data and workload conditions. For example, when workloads under utilize system resources, Zoolander’s model often suggests replication for predictability, strengthening service levels by reducing outlier response times. When workloads use system resources heavily, causing large queuing delays, Zoolander’s model suggests scaling out via traditional approaches. We used a diurnal trace to test Zoolander at scale (up to 40M accesses per hour). Zoolander reduced SLO violations by 32%.