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

TL;DR

This paper introduces Delta Fair Sharing algorithms to provide performance isolation in multi-tenant storage systems, bounding client delays and improving resource utilization.

Contribution

It proposes a novel family of algorithms satisfying delta-fairness and delta-Pareto-efficiency, ensuring bounded delays and high utilization in storage resource sharing.

Findings

FAIRDB effectively isolates clients from high-demand workloads.

Delta Fair Sharing reduces tail-latency spikes to delta time units.

The approach outperforms existing methods in resource isolation.

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.