基于近似匹配的移动边缘计算缓存管理方法

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 o...

Cache Management Method in Mobile Edge Computing Based on Approximate Matching

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.