Cache-Centric Multi-Resource Allocation for Storage Services

Abstract

We present HARE, a cache-centric multi-resource allocation algorithm for storage services. HARE introduces a holistic allocation model that captures the demand correlation between cache size and other resources (e.g., I/O, network), and uses a novel two-phase harvest/redistribute method to optimize resource allocation across tenants, maximizing the throughput of each while maintaining fairness. To demonstrate that HARE is widely applicable, we built two systems. The first, HopperKV, is a cloud-native key-value store that modifies Redis to cache data from DynamoDB. The second, BunnyFS, is a microkernel-style local filesystem for NVMe SSDs. Our evaluation shows that HARE is effective for multi-resource allocation in storage. Both systems are scalable and adaptive: HopperKV achieves up to a 1.9x performance improvement, and BunnyFS achieves up to 1.4x.