DmRPC: Disaggregated Memory-aware Datacenter RPC for Data-intensive Applications
Jie Zhang, Xuzheng Chen, Yin Zhang, Zeke Wang
摘要
Modern datacenter applications are increasingly being built using a microservices architecture. These microservices communicate with each other using datacenter RPCs. RPC's pass by value semantics incur redundant data movement along the network, especially for data-intensive applications. Naively introducing a shared global address space to datacenter RPC does not work as it would couple microservices and require microservices to handle data consistency, significantly complicating the development and deployment of applications. Fortunately, the modern datacenter is embracing disaggregated memory (DM). In a DM-enabled datacenter, servers running the microservices can be all connected to one global disaggregated memory pool, thus the pass by value semantics can be replaced by pass by reference. However, prior work on DM requires complicated synchronization primitives to share data across physical machines, so naively adopting them to datacenter RPC would harm microservices' agility and modularity. To this end, we present DmRPC, a DM-aware datacenter RPC for data-intensive datacenter applications to our knowledge. First, DmRPC introduces a DM-aware shared global address space to provide the semantics of pass by reference to datacenter RPC, thus alleviating the redundant data movement issue. Second, DmRPC adopts a copy-on-write mechanism to avoid complicating application logic to handle data consistency while guaranteeing high performance. We have applied DmRPC to two different implementations of DM, one is network-based (DmRPC-net) while the other is CXL-based (DmRPC-CXL). Our evaluations on synthetic 7-tier microservices workloads show that DmRPC-net (or DmRPC-CXL) achieves 4.2× (or 8.3×) higher throughput and achieves 1.1 × (or 1.7 ×) lower average latency than that of the baseline, respectively. On a widely used microservice benchmark DeathStarBench, DmRPC-net can achieve 3.1 × higher throughput and 2.5 × lower average latency than the baseline.
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引用它的顶会 Paper4
- Octopus: Enhancing CXL Memory Pods via Sparse TopologyYuhong Zhong, Fiodar Kazhamiaka, Pantea Zardoshti, Shuwei Teng 等NSDI 2026 · 被引用 15 次
- RpcNIC: Enabling Efficient Datacenter RPC Offloading on PCIe-attached SmartNICsJie Zhang, Hongjing Huang, Xuzheng Chen, Xiang Li 等HPCA 2025 · 被引用 6 次
- SwCC: Software-Programmable and Per-Packet Congestion Control in RDMA EngineHongjing Huang, Jie Zhang, Xuzheng Chen, Ziyu Song 等USENIX ATC 2025 · 被引用 4 次
- Cohet: A CXL-Driven Coherent Heterogeneous Computing Framework with Hardware-Calibrated Full-System SimulationYanjing Wang, Lizhou Wu, Sunfeng Gao, Yibo Tang 等HPCA 2026 · 被引用 1 次
它引用的顶会 Paper22
- Pond: CXL-Based Memory Pooling Systems for Cloud PlatformsHuaicheng Li, Daniel S. Berger, Lisa Hsu, Daniel Ernst 等ASPLOS 2023 · 被引用 328 次
- Catalyzer: Sub-millisecond Startup for Serverless Computing with Initialization-less BootingDong Du, Tianyi Yu, Yubin Xia, Binyu Zang 等ASPLOS 2020 · 被引用 280 次
- TPP: Transparent Page Placement for CXL-Enabled Tiered-MemoryHasan Al Maruf, Hao Wang, Abhishek Dhanotia, Johannes Weiner 等ASPLOS 2023 · 被引用 255 次
- When Cloud Storage Meets RDMAYixiao Gao, Qiang Li, Lingbo Tang, Yongqing Xi 等NSDI 2021 · 被引用 228 次
- Sinan: ML-based and QoS-aware resource management for cloud microservicesYanqi Zhang, Weizhe Hua, Zhuangzhuang Zhou, G. Edward Suh 等ASPLOS 2021 · 被引用 226 次
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