FlexKV: Flexible Index Offloading for Memory-Disaggregated Key-Value Store
Zhisheng Hu, Jiacheng Shen, Ming-Chang Yang

TL;DR
FlexKV introduces a dynamic index offloading approach for memory-disaggregated key-value stores, significantly enhancing performance by balancing load, optimizing memory use, and reducing cache coherence overhead.
Contribution
It presents a novel index proxying technique with load balancing, memory optimization, and cache management for improved DM-based KV store performance.
Findings
Up to 2.94× throughput improvement
Latency reduction of up to 85.2%
Effective load balancing across compute nodes
Abstract
Disaggregated memory (DM) is a promising data center architecture that decouples CPU and memory into independent resource pools to improve resource utilization. Building on DM, memory-disaggregated key-value (KV) stores are adopted to efficiently manage remote data. Unfortunately, existing approaches suffer from poor performance due to two critical issues: 1) the overdependence on one-sided atomic operations in index processing, and 2) the constrained efficiency in compute-side caches. To address these issues, we propose FlexKV, a memory-disaggregated KV store with index proxying. Our key idea is to dynamically offload the index to compute nodes, leveraging their powerful CPUs to accelerate index processing and maintain high-performance compute-side caches. Three challenges have to be addressed to enable efficient index proxying on DM, i.e., the load imbalance across compute nodes, the…
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Taxonomy
TopicsCloud Computing and Resource Management · Parallel Computing and Optimization Techniques · Big Data and Digital Economy
