Paper Info
Title
Working set size estimation with hugepages in virtualization
Abstract
With the rapid increase of data set size of cloud and big data applications, conventional regular 4KB pages can cause high pressure on hardware address translations. The pressure becomes more prominent in a virtualized system, which adds an additional layer of address translation. Virtual to physical address translations reply on a hardware Translation Lookaside Buffer (TLB) to cache address mappings. However, even modern hardware offers a very limited number of TLB entries. Meanwhile, TLB misses can cause significant performance degradation. Using 2MB or 1GB hugepages can improve TLB coverage and reduce TLB miss penalty. Therefore, recent operation systems, such as Linux, start to adopt hugepages. However, using hugepages bring new challenges, among which is working set size prediciton. In a virtualized system, working set size (WSS) estimation, which predicts the actual memory demand of a virtual machine, is often applied to guide virtual machine memory management and memory allocation. We find that traditional WSS estimation methods with regular pages cannot be simply ported to a system adopting hugepages. We estimate the working set size of a virtual machine by constructing a miss ratio curve (MRC), which relates page miss ratio to the virtual machine memory allocation. Using hugepages increases the overhead to track page accesses for MRC construction and also demands much higher precision in representing the miss ratios as a hugepage miss leads to a much higher penalty than a regular page miss. In this paper, we propose an accurate WSS estimation method in a virtual execution environment with hugepages. We design and implement a low overhead dynamic memory tracking mechanism by utilizing a hot set to filter frequent short-reuse accesses. Our approach is able to output a hugepage miss ratio at high precision. The experimental results show that our method can predict WSS accurately with an average overhead of 1.5%. © 2018 IEEE.
Year
DOI
Venue
2019
10.1109/BDCloud.2018.00081
Proceedings - 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018
Keywords
Field
DocType
Hugepage,Memory management,Performance,Virtualization,Working set size
Virtualization,Virtual machine,Physical address,Computer science,Cache,Human–computer interaction,Memory management,Translation lookaside buffer,Computer engineering,Working set size,Cloud computing
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Jingyuan Hu122.06
Xiaokuang Bai200.68
Sai Sha312.05
Yingwei Luo431541.30
Xiaolin Wang5176.26
Zhenlin Wang615015.89