DiFache: Efficient and Scalable Caching on Disaggregated Memory using Decentralized Coherence
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910964545224704 |
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| author | Zhang, Hanze Wang, Kaiming Chen, Rong Wei, Xingda Chen, Haibo |
| author_facet | Zhang, Hanze Wang, Kaiming Chen, Rong Wei, Xingda Chen, Haibo |
| contents | The disaggregated memory (DM) architecture offers high resource elasticity at the cost of data access performance. While caching frequently accessed data in compute nodes (CNs) reduces access overhead, it requires costly centralized maintenance of cache coherence across CNs. This paper presents DiFache, an efficient, scalable, and coherent CN-side caching framework for DM applications. Observing that DM applications already serialize conflicting remote data access internally rather than relying on the cache layer, DiFache introduces decentralized coherence that aligns its consistency model with memory nodes instead of CPU caches, thereby eliminating the need for centralized management. DiFache features a decentralized invalidation mechanism to independently invalidate caches on remote CNs and a fine-grained adaptive scheme to cache objects with varying read-write ratios. Evaluations using 54 real-world traces from Twitter show that DiFache outperforms existing approaches by up to 10.83$\times$ (5.53$\times$ on average). By integrating DiFache, the peak throughput of two real-world DM applications increases by 7.94$\times$ and 2.19$\times$, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiFache: Efficient and Scalable Caching on Disaggregated Memory using Decentralized Coherence Zhang, Hanze Wang, Kaiming Chen, Rong Wei, Xingda Chen, Haibo Distributed, Parallel, and Cluster Computing The disaggregated memory (DM) architecture offers high resource elasticity at the cost of data access performance. While caching frequently accessed data in compute nodes (CNs) reduces access overhead, it requires costly centralized maintenance of cache coherence across CNs. This paper presents DiFache, an efficient, scalable, and coherent CN-side caching framework for DM applications. Observing that DM applications already serialize conflicting remote data access internally rather than relying on the cache layer, DiFache introduces decentralized coherence that aligns its consistency model with memory nodes instead of CPU caches, thereby eliminating the need for centralized management. DiFache features a decentralized invalidation mechanism to independently invalidate caches on remote CNs and a fine-grained adaptive scheme to cache objects with varying read-write ratios. Evaluations using 54 real-world traces from Twitter show that DiFache outperforms existing approaches by up to 10.83$\times$ (5.53$\times$ on average). By integrating DiFache, the peak throughput of two real-world DM applications increases by 7.94$\times$ and 2.19$\times$, respectively. |
| title | DiFache: Efficient and Scalable Caching on Disaggregated Memory using Decentralized Coherence |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.18013 |