Tail Latency Under Concurrent Analytics, Checkpointing, and Replication: A Workload-Interference Study in Enterprise Cloud Storage
Abstract
Enterprise cloud storage commonly serves analytics, checkpointing, and replication at the same time, creating interference that may be invisible in average latency statistics. We reproduced 180 workload combinations on a shared storage platform and measured median, p95, and p99 latency, throughput, queue depth, and replication lag. Analytics scans had limited effect on median transaction latency until aggregate utilisation exceeded approximately 70%, after which p99 latency increased sharply. Concurrent checkpoint bursts and replication traffic produced the strongest interference, raising p99 write latency by 43% and extending replication backlog by a median of 18 minutes. Rate limiting background replication during checkpoint windows reduced tail-latency inflation to 14% with only a small increase in convergence time. Average throughput remained relatively stable in several high-interference conditions, masking substantial degradation at the tail. The study shows that capacity planning based on mean utilisation or throughput can underestimate contention risk. Tail-latency objectives, burst concurrency, and background-service scheduling should be incorporated into performance tests for shared enterprise storage.
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