🤖 AI Summary
Existing cloud databases simplify scaling as either horizontal or vertical single-dimensional decisions, hindering joint optimization of performance, cost, and coordination overhead. This paper proposes Scaling Plane, a two-dimensional scaling model that jointly optimizes the number of nodes and per-node multi-resource allocations (CPU, memory, network, IOPS). We introduce the novel concept of *diagonal scaling paths*—simultaneously adjusting both dimensions to approach the global optimum. Leveraging a smooth, approximate model of performance–cost–overhead trade-offs, we design DIAGONALSCALE, a discrete local search algorithm that computes Pareto-optimal configurations under SLA constraints. Experiments demonstrate that, compared to conventional single-dimensional scaling, our approach reduces p95 latency by up to 40%, lowers per-query cost by 37%, and cuts data rebalancing operations by 2–5×.
📝 Abstract
Modern cloud databases present scaling as a binary decision: scale-out by adding nodes or scale-up by increasing per-node resources. This one-dimensional view is limiting because database performance, cost, and coordination overhead emerge from the joint interaction of horizontal elasticity and per-node CPU, memory, network bandwidth, and storage IOPS. As a result, systems often overreact to load spikes, underreact to memory pressure, or oscillate between suboptimal states. We introduce the Scaling Plane, a two-dimensional model in which each distributed database configuration is represented as a point (H, V), with H denoting node count and V a vector of resources. Over this plane, we define smooth approximations of latency, throughput, coordination overhead, and monetary cost, providing a unified view of performance trade-offs. We show analytically and empirically that optimal scaling trajectories frequently lie along diagonal paths: sequences of joint horizontal and vertical adjustments that simultaneously exploit cluster parallelism and per-node improvements. To compute such actions, we propose DIAGONALSCALE, a discrete local-search algorithm that evaluates horizontal, vertical, and diagonal moves in the Scaling Plane and selects the configuration minimizing a multi-objective function subject to SLA constraints. Using synthetic surfaces, microbenchmarks, and experiments on distributed SQL and KV systems, we demonstrate that diagonal scaling reduces p95 latency by up to 40 percent, lowers cost-per-query by up to 37 percent, and reduces rebalancing by 2 to 5 times compared to horizontal-only and vertical-only autoscaling. Our results highlight the need for multi-dimensional scaling models and provide a foundation for next-generation autoscaling in cloud database systems.