Approximate Matching Based Cache Selection Strategy in Mobile Edge Computing

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 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, was proposed. The dynamic-LSH algorithm could deal with uneven data distribution by dynamically adjusting the hash bucket size accordingly, thereby selecting data like the input data from the cache space. Then, with distance and sample size as weights, the weighted-KNN algorithm reselected the data in the similar data sets acquired by the dynamic-LSH algorithm. From this approach, the data most like the input data was obtained, and the corresponding computing result was acquired for reuse. As demonstrated by simulation experiments, in the CIFAR-10 dataset, CSS-DLWK increased the average selection accuracy by 4.1 % compared to the cache selection strategy based on A-LSH and H-KNN algorithms. The improvement was 16.8% compared to traditional LSH algorithms. Overall, with acceptable time costs in data selection, the proposed strategy could effectively improve the selection accuracy of reusable data, thereby reducing repetitive computation in the edge server.