Clio: a hardware-software co-designed disaggregated memory system
Zhiyuan Guo, Yizhou Shan, Xuhao Luo, Yutong Huang, Yiying Zhang
Abstract
Memory disaggregation has attracted great attention recently because of its benefits in efficient memory utilization and ease of management. So far, memory disaggregation research has all taken one of two approaches: building/emulating memory nodes using regular servers or building them using raw memory devices with no processing power. The former incurs higher monetary cost and faces tail latency and scalability limitations, while the latter introduces performance, security, and management problems.
Server-based memory nodes and memory nodes with no processing power are two extreme approaches. We seek a sweet spot in the middle by proposing a hardware-based memory disaggregation solution that has the right amount of processing power at memory nodes. Furthermore, we take a clean-slate approach by starting from the requirements of memory disaggregation and designing a memory-disaggregation-native system.
We built Clio, a disaggregated memory system that virtualizes, protects, and manages disaggregated memory at hardware-based memory nodes. The Clio hardware includes a new virtual memory system, a customized network system, and a framework for computation offloading. In building Clio, we not only co-design OS functionalities, hardware architecture, and the network system, but also co-design compute nodes and memory nodes. Our FPGA prototype of Clio demonstrates that each memory node can achieve 100 Gbps throughput and an end-to-end latency of 2.5 𝜇𝑠 at median and 3.2 𝜇𝑠 at the 99th percentile. Clio also scales much better and has orders of magnitude lower tail latency than RDMA. It has 1.1× to 3.4× energy saving compared to CPU-based and SmartNIC-based disaggregated memory systems and is 2.7× faster than software-based SmartNIC solutions.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 30f62615-a12d-4659-bb3b-66ac7175a3e2Cited by top-tier papers46
- Pond: CXL-Based Memory Pooling Systems for Cloud PlatformsHuaicheng Li, Daniel S. Berger, Lisa Hsu, Daniel Ernst et al.ASPLOS 2023 · 328 citations
- Sherman: A Write-Optimized Distributed B+Tree Index on Disaggregated MemoryQing Wang, Youyou Lu, Jiwu ShuSIGMOD 2022 · 99 citations
- ROLEX: A Scalable RDMA-oriented Learned Key-Value Store for Disaggregated Memory SystemsPengfei Li, Yu Hua, Pengfei Zuo, Zhangyu Chen et al.FAST 2023 · 90 citations
- No Provisioned Concurrency: Fast RDMA-codesigned Remote Fork for Serverless ComputingXingda Wei, Fangming Lu, Tianxia Wang, Jinyu Gu et al.OSDI 2023 · 78 citations
- Overcoming the Memory Wall with CXL-Enabled SSDsShao-Peng Yang, Minjae Kim, Sanghyun Nam, Juhyung Park et al.USENIX ATC 2023 · 75 citations
Builds on16
- Swift: Delay is Simple and Effective for Congestion Control in the DatacenterGautam Kumar, Nandita Dukkipati, Keon Jang, Hassan M. G. Wassel et al.SIGCOMM 2020 · 333 citations
- When Cloud Storage Meets RDMAYixiao Gao, Qiang Li, Lingbo Tang, Yongqing Xi et al.NSDI 2021 · 228 citations
- AIFM: High-Performance, Application-Integrated Far MemoryZhenyuan Ruan, Malte Schwarzkopf, Marcos K. Aguilera, Adam BelayOSDI 2020 · 224 citations
- Can far memory improve job throughput?Emmanuel Amaro, Christopher Branner-Augmon, Zhihong Luo, Amy Ousterhout et al.EuroSys 2020 · 163 citations
- Building An Elastic Query Engine on Disaggregated StorageMidhul Vuppalapati, Justin Miron, Rachit Agarwal, Dan Truong et al.NSDI 2020 · 142 citations
Related papers
- Cowbird: Freeing CPUs to Compute by Offloading the Disaggregation of MemoryXinyi Chen, Liangcheng Yu, Vincent Liu, Qizhen ZhangSIGCOMM 2023 · 15 citations
- UniMem: Redesigning Disaggregated Memory within A Unified Local-Remote Memory HierarchyYijie Zhong, Minqiang Zhou, Zhirong Shen, Jiwu ShuUSENIX ATC 2024 · 7 citations
- ThymesisFlow: A Software-Defined, HW/SW co-Designed Interconnect Stack for Rack-Scale Memory DisaggregationChristian Pinto, Dimitris Syrivelis, Michele Gazzetti, Panos K. Koutsovasilis et al.MICRO 2020 · 61 citations
- Fast Distributed Transactions for RDMA-based Disaggregated MemoryHaodi Lu, Haikun Liu, Yujian Zhang, Zhuohui Duan et al.USENIX ATC 2025 · 9 citations
- Deft: A Scalable Tree Index for Disaggregated MemoryJing Wang, Qing Wang, Yuhao Zhang, Jiwu ShuEuroSys 2025 · 7 citations
