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AutoSLO is presented, a latency-SLO-aware workload management framework for multi-cluster cloud data warehouses that successfully meets latency SLOs of varying strictness, reducing cost by a mean of 26.4% compared to the per-scenario next-best baseline.
This work outlines AutoSLO, a latency-SLO-aware work-load management framework for multi-cluster cloud data ware-houses that includes a periodic Policy Tuner that proactively plans resources using workload fore-casts, an SLO-aware reactive Autoscaler that adjusts the active cluster set based on the observed workload, and an online Query Router that reacts to concurrent query load when routing.
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