CacheSack: Theory and Experience of Google's Admission Optimization for Datacenter Flash Caches

被引:1
作者
Yang, Tzu-Wei [1 ]
Pollen, Seth [2 ]
Uysal, Mustafa [1 ]
Merchant, Arif [1 ]
Wolfmeister, Homer [2 ]
Khalid, Junaid [2 ]
机构
[1] Google, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 USA
[2] Google, 811 E Washington Ave,Suite 700, Madison, WI 53703 USA
关键词
Flash caches; distributed storage systems;
D O I
10.1145/3582014
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This article describes the algorithm, implementation, and deployment experience of CacheSack, the admission algorithm for Google datacenter flash caches. CacheSack minimizes the dominant costs of Google's datacenter flash caches: disk IO and flash footprint. CacheSack partitions cache traffic into disjoint categories, analyzes the observed cache benefit of each subset, and formulates a knapsack problem to assign the optimal admission policy to each subset. Prior to this work, Google datacenter flash cache admission policies were optimized manually, with most caches using the Lazy Adaptive Replacement Cache algorithm. Production experiments showed that CacheSack significantly outperforms the prior static admission policies for a 7.7% improvement of the total cost of ownership, as well as significant improvements in disk reads (9.5% reduction) and flash wearout (17.8% reduction).
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页数:24
相关论文
共 44 条
[21]   Improving Flash-Based Disk Cache with Lazy Adaptive Replacement [J].
Huang, Sai ;
Wei, Qingsong ;
Feng, Dan ;
Chen, Jianxi ;
Chen, Cheng .
ACM TRANSACTIONS ON STORAGE, 2016, 12 (02)
[22]   Back to the Future: Leveraging Belady's Algorithm for Improved Cache Replacement [J].
Jain, Akanksha ;
Lin, Calvin .
2016 ACM/IEEE 43RD ANNUAL INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE (ISCA), 2016, :78-89
[23]   On the Convergence of the TTL Approximation for an LRU Cache under Independent Stationary Request Processes [J].
Jiang, Bo ;
Nain, Philippe ;
Towsley, Don .
ACM TRANSACTIONS ON MODELING AND PERFORMANCE EVALUATION OF COMPUTING SYSTEMS, 2018, 3 (04)
[24]   An In-Depth Analysis of Cloud Block Storage Workloads in Large-Scale Production [J].
Li, Jinhong ;
Wang, Qiuping ;
Lee, Patrick P. C. ;
Shi, Chao .
2020 IEEE INTERNATIONAL SYMPOSIUM ON WORKLOAD CHARACTERIZATION (IISWC 2020), 2020, :37-47
[25]  
Liu E. Z., 2020, PMLR
[26]   Kangaroo: Caching Billions of Tiny Objects on Flash [J].
McAllister, Sara ;
Berg, Benjamin ;
Tutuncu-Macias, Julian ;
Yang, Juncheng ;
Gunasekar, Sathya ;
Lu, Jimmy ;
Berger, Daniel S. ;
Beckmann, Nathan ;
Ganger, Gregory R. .
PROCEEDINGS OF THE 28TH ACM SYMPOSIUM ON OPERATING SYSTEMS PRINCIPLES, SOSP 2021, 2021, :243-262
[27]  
Megiddo N, 2003, USENIX ASSOCIATION PROCEEDINGS OF THE 2ND USENIX CONFERENCE ON FILE AND STORAGE TECHNOLOGIES (FAST'03), P115
[28]  
Mesnier M, 2011, SOSP 11: PROCEEDINGS OF THE TWENTY-THIRD ACM SYMPOSIUM ON OPERATING SYSTEMS PRINCIPLES, P57
[29]  
Min Changwoo, 2012, FAST
[30]  
Pancham Pancham, 2014, INT J COMPUT APPL, V98, P27, DOI [10.5120/17293-7771, DOI 10.5120/17293-7771]