Cache of cryptographic key based on query popularity

Wei JIN; Fenghua LI; Ziyan ZHOU; Xiyang SUN; Yunchuan GUO

doi:10.11959/j.issn.2096-109x.2023008

您当前的位置：

首页 >

文章列表页 >

Cache of cryptographic key based on query popularity

Papers | 更新时间：2024-06-03

- Cache of cryptographic key based on query popularity
- Chinese Journal of Network and Information Security Vol. 9, Issue 1, Pages: 83-91(2023)
- 作者机构：
  
  1. 中国科学院信息工程研究所，北京 100093
  2. 中国科学院大学网络空间安全学院，北京 100049
  3. 中国信息通信研究院，北京 100191
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2023008
  CLC： TP393
- Online First：2023-02，
  
  Published：25 February 2023
- 稿件说明：
移动端阅览
Wei JIN, Fenghua LI, Ziyan ZHOU, et al. Cache of cryptographic key based on query popularity[J]. Chinese Journal of Network and Information Security, 2023, 9(1): 83-91.
DOI：

Wei JIN, Fenghua LI, Ziyan ZHOU, et al. Cache of cryptographic key based on query popularity[J]. Chinese Journal of Network and Information Security, 2023, 9(1): 83-91. DOI： 10.11959/j.issn.2096-109x.2023008.

摘要

在当前的分布式文件系统（HDFS，Hadoop distributed file system）密钥管理系统中，加密区密钥在启动时全部加载至内存，提供密钥服务。随着密钥资源的增加，占据的内存空间也随之增长，带来内存空间不足和密钥索引瓶颈，如何组织缓存数据、高效处理未命中密钥的查询，如何调整缓存中的密钥资源，如何精准预测密钥的使用这三大要点是解决该瓶颈的关键所在。为了实现细粒度高效缓存，提高密钥使用效率，从密钥索引数据结构、密钥置换算法、密钥预取策略分析3个方面出发，设计了密钥缓存置换的模块架构，计算密钥热度，设置密钥置换算法。具体地，在密钥热度计算与缓存置换方面，从密钥所绑定的文件系统和用户出发，分析影响密钥缓存热度的潜在影响要素，构建密钥使用热度的基本模型，采用哈希表与小顶堆链表组合的方式，维护在用密钥的热度，基于热度识别设置淘汰算法，由时间控制器调整密钥使用，动态更新缓存中的密钥，从而实现基于热度计算的密钥差异化置换。在密钥预取策略分析方面，综合考虑业务流程和用户访问存在时间周期维度的规律，通过日志挖掘获取密钥使用规律，分析密钥预置策略。实验表明，所提密钥置换算法可在降低内存占用的同时，有效提升缓存命令率和密钥查询效率，降低密钥文件I/O交互对查询性能的影响。

Abstract

In the current HDFS (Hadoop Distributed File System) key management system

the encryption zone keys are all loaded into the memory during startup of key service.With the increase of the key resource

the occupied memory space also grows

bringing the bottleneck of memory space and key indexing.There are three challenges induced: how to organize cached data and efficiently handle queries with missed keys

how to adjust key resources in the cache

and how to accurately predict the use of keys.In order to achieve fine-grained and efficient caching and improve the efficiency of key use

key caching optimization was considered from three aspects: key index data structure

key replacement algorithm

and key prefetching strategy.An architecture of key cache replacement module was designed

and then a key replacement algorithm based on the query frequency was set.Specifically

from the perspective of heat computing and key replacement

the potential influencing factors affecting the popularity of key cache were analyzed which considered the file system and user of key management system.Besides

the basic model of key usage popularity was constructed.The hash table and minheap linked list was combined to maintain the heat of the key in use

and the elimination algorithm was set based on heat identification.The key in the cache was dynamically updated

and key usage was adjusted by the time controller

so as to realize key replacement according to the key heat.For key prefetching

key usage rules were obtained through log mining and periodical usage analyzing of key provisioning policies

which considered business processes and the time period dimension of user accessing.Experimental results show that the proposed key replacement algorithm can effectively improve the hit rate of cache queries

reduce memory usage

and ameliorate the impact of key file I/O interaction on query performance.

关键词

Keywords

references

第48次中国互联网络发展状况统计报告 [R ] . 中国互联网络信息中心 . 2021 .

The 48th statistical report on the development of China's internet [R ] . China Internet Network Information Center . 2021 .

SHIHABUL H M , PEDDERSEN J , JANAPSATYA A , et al . DEW:a fast level 1 cache simulation approach for embedded processors with FIFO replacement policy [C ] // Proceedings of 2010 Design,Automation ＆ Test in Europe Conference ＆ Exhibition (DATE 2010) . 2010 : 496 - 501 .

INOUE H , . Multi-step LRU:SIMD-based cache replacement for lower overhead and higher precision [C ] // Proceedings of 2021 IEEE International Conference on Big Data . 2022 : 174 - 180 .

ZHENG Q , YANG T , KAN Y Z , et al . On the analysis of cache invalidation with LRU replacement [J ] . IEEE Transactions on Parallel and Distributed Systems , 2022 , 33 ( 3 ): 654 - 666 .

MATANI D , SHAH K , MITRA A . An O (1) algorithm for implementing the LFU cache eviction scheme [J ] . arXiv preprint arXiv:2110.11602 , 2021 .

LIU Y , ZHI T , XI H D , et al . A novel cache replacement scheme against cache pollution attack in content-centric networks [C ] // Proceedings of 2019 IEEE/CIC International Conference on Communications in China (ICCC) . 2019 : 207 - 212 .

HASSAN C A U , HAMMAD M , UDDIN M , et al . Optimizing the performance of data warehouse by query cache mechanism [J ] . IEEE Access , 2022 , 10 : 13472 - 13480 .

BALAJI V , CRAGO N , JALEEL A , et al . P-OPT:practical optimal cache replacement for graph analytics [C ] // Proceedings of 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA) . 2021 : 668 - 681 .

CHEN H , XIAO Y , VRBSKY S V . An update-based step-wise optimal cache replacement for wireless data access [J ] . Computer Networks , 2013 , 57 ( 1 ): 197 - 212 .

PRIYA B K , KUMAR S , BEGUM S , et al . Enhancing the lifetime of STT-RAM with MRU replacement algorithm [C ] // Proceedings of 4th International Conference on Recent Advances in Information Technology (RAIT) . 2018 : 1 - 6 .

MEGIDDO N , MODHA D S . ARC:a self-tuning,low overhead replacement cache [C ] // Proceedings of the 2nd USENIX Conference on File and Storage Technologies( FAST'03) . 2003 :9.

MEINT D , LIEBALD S . From FIFO to predictive cache replacement [J ] . Network , 2019 ( 25 ).

BUTT A R , GNIADY C , HU Y C . The performance impact of kernel prefetching on buffer cache replacement algorithms [C ] // Proceedings of IEEE Transactions on Computers . 2007 : 889 - 908 .

OSMAN A M , OSMAN N I . A comparison of cache replacement algorithms for video services [J ] . International Journal of Computer Science and Information Technology , 2018 , 10 ( 2 ): 95 - 111 .

刘恒 , 谭良 . 并行计算框架Spark中一种新的RDD分区权重缓存替换算法 [J ] . 小型微型计算机系统 , 2018 , 39 ( 10 ): 2279 - 2284 .

LIU H , TAN L . New RDD partition weight cache replacement algorithm in spark [J ] . Journal of Chinese Computer Systems , 2018 , 39 ( 10 ): 2279 - 2284 .

LEVANDOSKI J J , LARSON P Å , STOICA R . Identifying hot and cold data in main-memory databases [C ] // Proceedings of 2013 IEEE 29th International Conference on Data Engineering (ICDE) . Piscataway:IEEE Press , 2013 : 26 - 37 .

陈建 , 沈潇军 , 姚一杨 , 等 . 基于密文策略属性基加密系统访问机制的缓存替换策略 [J ] . 计算机应用 , 2017 , 37 ( 10 ): 2964 - 2967 .

CHEN J , SHEN X J , YAO Y Y , et al . Cache replacement strategy based on access mechanism of ciphertext policy attribute based encryption [J ] . Journal of Computer Applications , 2017 , 37 ( 10 ): 2964 - 2967 .

葛微 , 罗圣美 , 周文辉 , 等 . HiBase:一种基于分层式索引的高效HBase查询技术与系统 [J ] . 计算机学报 , 2016 , 39 ( 1 ): 140 - 153 .

GE W , LUO S M , ZHOU W H , et al . HiBase:a hierarchical indexing mechanism and system for efficient HBase query [J ] . Chinese Journal of Computers , 2016 , 39 ( 1 ): 140 - 153 .

王文楚 . 基于相变存储器的平行架构混合内存性能优化研究 [D ] . 武汉:华中科技大学 , 2017 .

WANG W C . Improving the performance of parallel organization of hybrid memory system based on phase change memory [D ] . Wuhan:Huazhong University of Science and Technology , 2017 .

李崇杰 . 分布式文件系统缓存技术研究 [D ] . 南京:南京大学 , 2019 .

LI C J . Research on cache techniques for distributed file system [D ] . Nanjing:Nanjing University , 2019 .

JIN W , GENG K , YU M J , et al . Efficiently managing large-scale keys in HDFS [C ] // Proceedings of 2021 IEEE 23rd Int Conf on High Performance Computing ＆ Communications; 7th Int Conf on Data Science ＆ Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor,Cloud ＆ Big Data Systems ＆Application (HPCC/ DSS/SmartCity/DependSys) . Piscataway:IEEE Press , 2022 : 353 - 360 .

Views

416

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Survey of key management schemes for cluster networks

Blockchain-based key management scheme for distributed networks

Research on key management scheme for military vehicle network

Research on key management and authentication protocol of PDA in smart grid

Key management scheme with private key self-healing ability in DTN

Related Author

Zhishang FENG

Lin YUAN

Jun LIU

Zhicheng ZHOU

Guoming CAI

Song GUO

Kaiyong XU

Qianyi DAI

Related Institution

School of Cryptographic Engineering,Strategic Support Force Information Engineering University

Troops

Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University

School of Computer and Information Technology, Beijing Jiaotong University

Beijing Municipal Public Security Bureau Network Security and Security Corps

⁰