UKSM: Swift Memory Deduplication via Hierarchical and Adaptive Memory Region Distilling

Authors: 

Nai Xia and Chen Tian, State Key Laboratory for Novel Software Technology, Nanjing University, China; Yan Luo and Hang Liu, Department of Electrical and Computer Engineering, University of Massachusetts Lowell, USA; Xiaoliang Wang, State Key Laboratory for Novel Software Technology, Nanjing University, China

Abstract: 

In cloud computing, deduplication can reduce memory footprint by eliminating redundant pages. The responsiveness of a deduplication process to newly generated memory pages is critical. State-of-the-art Content Based Page Sharing (CBPS) approaches lack responsiveness as they equally scan every page while finding redundancies. We propose a new deduplication system UKSM, which prioritizes different memory regions to accelerate the deduplication process and minimize application penalty. With UKSM, memory regions are organized as a distilling hierarchy, where a region in a higher level receives more CPU cycles. UKSM adaptively promotes/demotes a region among levels according to the region’s estimated deduplication benefit and penalty. UKSM further introduces an adaptive partial-page hashing scheme which adjusts a global page hashing strength parameter according to the global degree of page similarity. Experiments demonstrate that, with the same amount of CPU cycles in the same time envelop, UKSM can achieve up to 12.6and 5 more memory saving than CBPS approaches on static and dynamic workloads, respectively.

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BibTeX
@inproceedings {210534,
author = {Nai Xia and Chen Tian and Yan Luo and Hang Liu and Xiaoliang Wang},
title = {{UKSM}: Swift Memory Deduplication via Hierarchical and Adaptive Memory Region Distilling},
booktitle = {16th {USENIX} Conference on File and Storage Technologies ({FAST} 18)},
year = {2018},
isbn = {978-1-931971-42-3},
address = {Oakland, CA},
pages = {325--340},
url = {https://www.usenix.org/conference/fast18/presentation/xia},
publisher = {{USENIX} Association},
}