Magneto: Load-Balanced Key-Value Service for Write-Intensive Workloads
High-performance key-value (KV) storage is critical for the cloud, providing KV services to various cloud applications. A key challenge for KV services is that workloads of cloud applications are often write-intensive and exhibit highly-skewed characteristics, which result in load imbalance among storage servers thus lowering system performance. To address this problem, prior works set up an in-switch write-back caching mechanism, which adopts a centralized controller to balance writes. However, the limited bandwidth between the switches and the controller is a severe bottleneck for achieving high performance. In this paper, we present Magneto, a novel key-value service architecture for the cloud. At the core of Magneto is a delayed-write mechanism in the switch data plane to absorb frequent write queries for hot items. It effectively balances the load under write-intensive workloads without involving the controller. Magneto also designs a reliability mechanism to ensure system reliability during switch state transitions and failures. We implement a prototype using an FPGA-integrated switch, which has high packet-processing performance and contains enough memory to provide both the in-switch cache and the write buffer. Extensive evaluation shows that Magneto can achieve 8.4x system throughput gains compared to baseline systems when handling a skewed workload consisting of 70% reads and 30% writes. Moreover, it can reduce the load on back-end servers up to 56% in total.
Authors
- Ming Xu (ORCID: https://orcid.org/0000-0003-0832-6067)
- Huan Zhou (ORCID: https://orcid.org/0000-0003-2319-4103)
- Gao Yan (ORCID: https://orcid.org/0000-0002-3375-9668)
- Yingwen Chen (ORCID: https://orcid.org/0000-0002-8171-1861)
- Shihua Tang (ORCID: https://orcid.org/0000-0002-2757-0332)
- Xiangrui Yang (ORCID: https://orcid.org/0009-0008-7655-1948)
Institutions
- National University of Defense Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Services Computing
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1109/tsc.2025.3592425
- Primary Topic
- Distributed and Parallel Computing Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00