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UCat: heterogeneous memory management for unikernels

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Abstract

Unikernels provide an efficient and lightweight way to deploy cloud computing services in application-specialized and single-address-space virtual machines (VMs). They can efficiently deploy hundreds of unikernel-based VMs in a single physical server. In such a cloud computing platform, main memory is the primary bottleneck resource for high-density application deployment. Recently, non-volatile memory (NVM) technologies has become increasingly popular in cloud data centers because they can offer extremely large memory capacity at a low expense. However, there still remain many challenges to utilize NVMs for unikernel-based VMs, such as the difficulty of heterogeneous memory allocation and high performance overhead of address translations.

In this paper, we present UCat, a heterogeneous memory management mechanism that support multi-grained memory allocation for unikernels. We propose front-end/back-end cooperative address space mapping to expose the host memory heterogeneity to unikernels. UCat exploits large pages to reduce the cost of two-layer address translation in virtualization environments, and leverages slab allocation to reduce memory waste due to internal memory fragmentation. We implement UCat based on a popular unikernel—OSv and conduct extensive experiments to evaluate its efficiency. Experimental results show that UCat can reduce the memory consumption of unikernels by 50% and TLB miss rate by 41%, and improve the throughput of real-world benchmarks such as memslap and YCSB by up to 18.5% and 14.8%, respectively.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62072198, 61732010, 61825202, and 62032008).

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Authors and Affiliations

  1. National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab/Cluster and Grid Computing Lab, School of Computing Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Chong Tian, Haikun Liu, Xiaofei Liao & Hai Jin

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  1. Chong Tian
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  2. Haikun Liu
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  3. Xiaofei Liao
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  4. Hai Jin
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Corresponding author

Correspondence to Haikun Liu.

Additional information

Chong Tian received his BS degree and MS degree in computer science and technology from Huazhong University of Science and Technology (HUST), China in 2018 and 2021, respectively. His research interests include hybrid memory and virtualization technologies.

Haikun Liu is a professor in the School of Computer Science and Technology, Huazhong University of Science and Technology, China. He received his PhD degree in computer science and technology from HUST, China in 2012. His current research interests include in-memory computing, virtualization technologies, cloud computing, and distributed systems. He is a senior member of CCF and a member of the IEEE.

Xiaofei Liao is a professor in the School of Computer Science and Technology at Huazhong University of Science and Technology (HUST), China. He received his PhD degree in computer science and engineering from HUST, China in 2005. He was awarded Excellent Youth Award from the National Science Foundation of China in 2018 and CCF-IEEE CS Young Computer Scientist Award in 2017. His research interests are in the areas of computer architecture, system software, and big data processing. He is a member of IEEE and the IEEE Computer Society.

Hai Jin is a Chair Professor of computer science and engineering at Huazhong University of Science and Technology (HUST), China. Jin received his PhD in computer engineering from HUST, China in 1994. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany. He worked at The University of Hong Kong, China between 1998 and 2000, and as a visiting scholar at the University of Southern California, USA between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. He is a Fellow of IEEE, a Fellow of CCF, and a life member of the ACM. He has co-authored more than 20 books and published over 900 research papers. His research interests include computer architecture, parallel and distributed computing, big data processing, data storage, and system security.

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Tian, C., Liu, H., Liao, X. et al. UCat: heterogeneous memory management for unikernels. Front. Comput. Sci. 17, 171204 (2023). https://doi.org/10.1007/s11704-022-1201-y

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