AutoSLO: Practical Latency SLOs on Cloud Data Warehouses -- Extended Version

2026-07-13

AI Summary

This work addresses the inefficiencies in multi-cluster cloud data warehouses, where static or over-provisioned resource allocation often leads to excessive costs and violations of latency service-level objectives (SLOs). To tackle this, the authors propose AutoSLO, a novel framework that integrates historical workload forecasting, real-time responsive scaling, and concurrency-aware query routing to jointly optimize resource cost and SLO compliance across multiple timescales. AutoSLO features a three-tier architecture comprising a policy tuner leveraging one-day historical data, an SLO-aware autoscaler, and an online query router. Experimental evaluation on Redbench demonstrates that AutoSLO reduces average costs by 26.4%, with the query router and autoscaler lowering SLO violation rates by 47.8% and 93.7%, respectively, while the policy tuner alone achieves a 44.6% reduction in violations using only a single day of historical data.

Abstract

Modern cloud data warehouses decouple compute from storage, making it easy for organizations to access the same underlying data with multiple compute clusters. This flexibility is often used for performance isolation among diverse workloads, so that each workload meets its latency service-level objective (SLO) more reliably. For example, interactive dashboards, ad hoc analysis, and batch jobs can each run on separate clusters. However, this dedicated-cluster approach requires each compute cluster to be continuously scaled to adapt to workload evolution, with over-provisioning wasting resources and under-provisioning risking SLO violations. We present AutoSLO, a latency-SLO-aware workload management framework for multi-cluster cloud data warehouses. AutoSLO operates across three timescales through three key components. First, a periodic Policy Tuner plans proactive cluster scaling actions and tunes configuration parameters, using simulations of history-derived workload forecasts. Second, an SLO-aware reactive Autoscaler adjusts the active cluster set when recent workload behavior deviates from the forecast. Third, an online Query Router reacts to live load when placing each query, using a concurrency-aware latency predictor to avoid SLO violations. On realistic Redbench workloads, AutoSLO successfully meets latency SLOs of varying strictness, reducing cost by a mean of 26.4% compared to the per-scenario next-best baseline. Component-level evaluations show that the Query Router and Autoscaler respectively reduce SLO violation rates by a mean of 47.8% and 93.7%, relative to their corresponding alternatives. Finally, we show that the Policy Tuner can reduce the SLO violation rate by a mean of 44.6% using a single day of workload history, and that each component is efficient given its intended operating timescale.

Problem

• latency SLO
• cloud data warehouses
• workload management
• cluster scaling
• SLO violation

Innovation

• AutoSLO
• latency SLO
• cloud data warehouse
• workload management
• autoscaling