Delta Fair Sharing: Performance Isolation for Multi-Tenant Storage Systems

2026-01-27

AI Summary

This work addresses the challenge of performance isolation in multi-tenant storage systems, where traditional fair-sharing mechanisms suffer from high preemption latency, leading to severe tail latency spikes. To overcome this, the authors propose the Delta Fair Sharing family of algorithms, which introduces δ-fairness and δ-Pareto efficiency to enable bounded-latency fair sharing on latency-sensitive resources such as write buffers and read caches. The approach strictly confines tail latency spikes for well-behaved clients within δ time units while preserving high resource utilization. Implemented in FAIRDB—a RocksDB-based system—the algorithm supports resource scheduling with explicit latency bounds. Experimental results demonstrate that FAIRDB significantly outperforms existing solutions in isolating interference from high-load tenants, effectively safeguarding the performance of normal clients.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Modern storage systems, often deployed to support multiple tenants in the cloud, must provide performance isolation. Unfortunately, traditional approaches such as fair sharing do not provide performance isolation for storage systems, because their resources (e.g., write buffers and read caches) exhibit high preemption delays. These delays lead to unacceptable spikes in client tail latencies, as clients may be forced to wait arbitrarily long to receive their fair share of resources. We introduce Delta Fair Sharing, a family of algorithms for sharing resources with high preemption delays. These algorithms satisfy two key properties: $\delta$-fairness, which bounds a client's delay in receiving its fair share of resources to $\delta$ time units, and $\delta$-Pareto-efficiency, which allocates unused resources to clients with unmet demand. Together, these properties capture resource-acquisition delays end-to-end, bound well-behaved clients'tail-latency spikes to $\delta$ time units, and ensure high utilization. We implement such algorithms in FAIRDB, an extension of RocksDB. Our evaluation shows that FAIRDB isolates well-behaved clients from high-demand workloads better than state-of-the-art alternatives.

Problem

• performance isolation
• multi-tenant storage systems
• fair sharing
• tail latency
• preemption delay

Innovation

• Delta Fair Sharing
• performance isolation
• multi-tenant storage
• tail latency
• resource allocation