Taiji: A DPU Memory Elasticity Solution for In-production Cloud Environments
Authors
Hao Zheng
Longxiang Wang
Yun Xu
Qiang Wang
Yibin Shen
Xiaoshe Dong
Bang Di
Jia Wei
Shenyu Dong
Xingjun Zhang
Weichen Chen
Zhao Han
Sanqian Zhao
Dongdong Huang
Jie Qi
Yifan Yang
Zhao Gao
Yi Wang
Jinhu Li
Xudong Ren
Min He
Hang Yang
Xiao Zheng
Haijiao Hao
Jiesheng Wu
Abstract
The growth of cloud computing drives data centers toward higher density and efficiency. Data processing units (DPUs) enhance server network and storage performance but face challenges such as long hardware upgrade cycles and limited resources. To address these, we propose Taiji, a resource-elasticity architecture for DPUs. Combining hybrid virtualization with parallel memory swapping, Taiji switches the DPU's operating system (OS) into a guest OS and inserts a lightweight virtualization layer, making nearly all DPU memory swappable. It achieves memory overcommitment for the switched guest OS via high-performance memory elasticity, fully transparent to upper-layer applications, and supports hot-switch and hot-upgrade to meet in-production cloud requirements. Experiments show that Taiji expands DPU memory resources by over 50%, maintains virtualization overhead around 5%, and ensures 90% of swap-ins complete within 10 microseconds. Taiji delivers an efficient, reliable, low-overhead elasticity solution for DPUs and is deployed in large-scale production systems across more than 30,000 servers.
