计算机科学 ›› 2021, Vol. 48 ›› Issue (1): 96-102.doi: 10.11896/jsjkx.200800215

• 智能化边缘计算* 上一篇    下一篇

基于近似匹配的移动边缘计算缓存管理方法

郦睿翔, 毛莺池, 郝帅   

  1. 河海大学计算机与信息学院 南京 211100
  • 收稿日期:2020-08-30 修回日期:2020-11-07 出版日期:2021-01-15 发布日期:2021-01-15
  • 通讯作者: 毛莺池(yingchimao@hhu.edu.com)
  • 作者简介:loye317@sina.com
  • 基金资助:
    国家重点研发计划(2018YFC0407105);国家自然科学基金重点项目(61832005);华能集团重点研发课题(HNKJ17-21)

Cache Management Method in Mobile Edge Computing Based on Approximate Matching

LI Rui-xiang, MAO Ying-chi, HAO Shuai   

  1. School of Computer and Information,Hohai University,Nanjing 211100,China
  • Received:2020-08-30 Revised:2020-11-07 Online:2021-01-15 Published:2021-01-15
  • About author:LI Rui-xiang,born in 1996,master candidate,is a student member of China Computer Federation.His main research interests include edge computing,and federated learning.
    MAO Ying-chi,born in 1976,Ph.D,professor,is a senior member of China Computer Federation.Her main research interests include cloud computing and edge computing,mobile sen-sing systems and internet of things.
  • Supported by:
    National Key R&D Program of China(2018YFC0407105),Key Project of National Nature Science Foundation of China(61832005) and Key Technology Project of China Huaneng Group (HNKJ17-21).

摘要: 针对终端用户产生大量相同或相似计算请求的情况,可以通过近似匹配在边缘服务器缓存空间中查找相似数据,选取可复用的计算结果。现有算法大多未考虑数据分布不均的问题,导致计算量和时间开销较大,对此文中提出基于动态局部敏感哈希算法与加权k近邻算法的缓存数据选择策略(Cache Selection Strategy based on Dynamic-LSH algorithm and Weighted-KNN algorithm,CSS-DLWK)。其中,Dynamic-LSH算法能够针对数据分布不均的问题,根据数据分布的变化动态调整哈希桶粒度,从缓存空间中选出与输入数据相似的数据集合;Weighted-KNN算法以距离和样本数为权重,对由Dynamic-LSH算法获取的相似数据集合进行数据再选取,得到与输入数据最相似的数据,获取相应的计算结果以供复用。仿真实验结果表明,在CIFAR-10数据集中,与基于A-LSH算法与H-KNN算法的缓存选取策略相比,CSS-DLWK策略的平均选取准确率提高了4.1%;与传统的LSH算法相比,其平均选取准确率提高了16.8%。CSS-DLWK策略能够在可接受的数据选取时间开销内,有效地提高可复用数据选取的准确率,从而减少边缘服务器的重复计算。

关键词: 移动边缘计算, 缓存替换, 近似匹配, 数据复用, 局部敏感哈希算法

Abstract: For the case of massive identical or similar computing requests from end users,a search of similar data in the cache space of the edge server by approximate match can be applied to select computing results that can be reused.Most of the existing algorithms do not consider the uneven distribution of data,resulting in a large amount of calculation and time overhead.In this paper,a cache selection strategy based on dynamic-locality sensitive hashing (LSH) algorithm and Weighted-k nearest neighbor (KNN) algorithm(CSS-DLWK) is proposed.The Dynamic-LSH algorithm can deal with uneven data distribution by dynamically adjusting the hash bucket size accordingly,thereby selecting data sets that are similar to the input data from the cache space.Then,regarding distance and sample size as weights,the weighted-KNN algorithm re-selects the data in the similar data sets acquired by the dynamic-LSH algorithm.From this approach,the data most similar to the input data are obtained,and the corresponding computing result is acquired for reuse.As demonstrated by simulation experiments,in the CIFAR-10 dataset,CSS-DLWK increases the ave-rage selection accuracy by 4.1% compared to the cache selection strategy based on A-LSH and H-KNN algorithms.The improvement is 16.8% compared to traditional LSH algorithms.Overall,with acceptable time costs in data selection,the proposed strategy can effectively improve the selection accuracy of reusable data,thereby reducing repetitive computation in the edge server.

Key words: Mobile edge computing, Cache replacement, Approximate matching, Data reuse, Locality sensitive hashing algorithm

中图分类号: 

  • TP399
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