SAM: A Stability-Aware Cache Manager for Multi-Tenant Embedded Databases

TL;DR

SAM is a novel cache management system for multi-tenant embedded databases that enhances stability and robustness under dynamic workloads by balancing exploitation and exploration strategies.

Contribution

It introduces a stability-aware cache management framework with an adaptive control policy that ensures consistent performance and decision stability in volatile, resource-constrained environments.

Findings

Outperforms 14 baselines in throughput and stability

Maintains decision stability under workload shifts and adversarial conditions

Decision latency remains nearly constant as system scales to 120 databases

Abstract

The co-location of multiple database instances on resource constrained edge nodes creates significant cache contention, where traditional schemes are inefficient and unstable under dynamic workloads. To address this, we present SAM(a Stability-Aware Manager), an autonomic cache manager that establishes decision stability as a first-class design principle. It achieves this through its core control policy, AURA(Autonomic Utility-balancing Resource Allocator), which resolves the classic exploitation-exploration dilemma by synthesizing two orthogonal factors: the H-factor, representing proven historical efficiency (exploitation), and the V-factor, for estimated marginal gain (exploration). Through this practical synthesis and adaptive control, SAM achieves sustained high performance with strategic stability and robustness in volatile conditions. Extensive experiments against 14 diverse baselines demonstrate SAM's superiority. It achieves top-tier throughput while being uniquely resilient to complex workload shifts and adversarial workloads like cache pollution. Furthermore, its decision latency is highly scalable, remaining nearly constant as the system grows to 120 databases. Crucially, SAM achieves superior decision stability -- maintaining consistent optimization directions despite noise, avoiding performance oscillations while ensuring predictable Quality of Service. These results prove that a principled, stability-aware design is essential for sustained high performance in real-world, large-scale systems.