PLB: Priority-Aware Load Balancing for Replicated Databases under Constrained Resources

2026-08-06

AI Summary

This work addresses the challenge of achieving both high-priority performance guarantees and controlled degradation for low-priority requests in replicated database systems with fixed, non-scalable resources. The authors propose PLB, a priority-aware load balancer implemented at the JDBC driver layer, which combines static replica partitioning with a dynamic, load-aware capacity borrowing mechanism. High-priority requests can opportunistically utilize idle resources, while performance degradation for low-priority requests is effectively bounded. Experimental results demonstrate that, compared to static partitioning, PLB increases CPU utilization from 35% to over 70%. Against round-robin scheduling, PLB reduces median latency for high-priority requests by 12% on average (up to 28%) with only an ~11% increase in low-priority overhead, while maintaining overall latency close to round-robin levels—significantly enhancing both resource efficiency and differentiated quality of service.

Abstract

Priority-differentiated services are a standard way for applications to offer different levels of performance, but database systems still often treat all sessions the same way. When database capacity is fixed, meaning replicas cannot be added on demand, and the workload becomes contended, this creates a difficult trade-off between over-provisioning resources and letting lower-priority users experience much larger slowdowns. In such settings, we propose enforcing priority by controlling how client sessions are assigned to database replicas. We present PLB, a priority-aware load balancer implemented as a JDBC driver that enforces priority differentiation through replica assignment under fixed resources. PLB partitions replicas by user group, premium versus freemium, and uses load-based borrowing so that higher-priority users can use idle capacity when available, while degradation for lower-priority users remains controlled. We evaluate PLB on a replicated read-only cluster under OLAP workloads. Compared with static dedicated per-priority partitions, PLB keeps utilization above about 70% in settings where fixed partitions can reduce cluster-wide CPU utilization to about 35%, while maintaining latencies close to those of the best dedicated allocation. Compared with a fully shared round-robin pool, PLB lowers high-priority median latency by about 12% on average, with improvements of up to 28%, while keeping the low-priority median overhead around 11% and overall median latency close to round-robin.

Problem

• priority-aware
• load balancing
• replicated databases
• constrained resources
• service differentiation

Innovation

• priority-aware load balancing
• replicated databases
• resource-constrained systems
• JDBC driver
• latency differentiation