兼顾个性化需求的云边协作两级内容缓存研究

doi:10.19678/j.issn.1000-3428.0065809

摘要/Abstract

摘要： 针对用户访问移动短视频响应时延过长、不能满足个性化需求等问题，基于短视频的时延敏感性、个性化需求等特点，综合考虑短视频的内容流行度和用户偏好，提出一种云边协作环境下的两级内容缓存方案。根据用户对短视频偏好的平均值表征边缘节点的偏好值，进而计算边缘节点的相似度，综合考虑边缘节点之间的物理距离和相似度对节点协作的影响，建立边缘节点的协作节点集。基于长尾理论提出一种两级内容缓存策略，将每个边缘节点分为流行内容缓存区和用户偏好内容缓存区两部分，流行内容缓存区采取主动缓存策略,针对用户偏好内容缓存区的缓存内容，综合分析用户访问请求在不同响应方式下的延迟，并以最小化整体内容请求延迟为目标，设计一种基于改进离散蛙跳算法的边缘协作缓存方案。实验结果表明，在同一数据集上与RC、BEP等缓存方案相比，该方案的用户请求命中率提高近40%，并能够降低回程链路负载，减少用户请求延迟，满足时延敏感性特点及90%的用户个性化需求。

关键词: 边缘缓存, 云边协作, 边缘计算, 时延敏感性, 短视频缓存, 个性化需求

Abstract: In recent years，there have been problems such as the long response delay in user access to mobile short videos and an inability to meet personalized requirements，based on the problem of delay sensitivity and personalized requirements of short video.This paper proposes a two-level content caching scheme in the cloud-edge collaboration environment，considering in detail the content popularity of short video and user preference.First，the similarity of edge nodes is calculated according to the user preference value，and the cooperative node set of edge nodes is established considering in detail the influence of physical distance and similarity between edge nodes on node cooperation. Secondly，this paper proposes a two-level content caching strategy based on the long tail theory，and each edge node is divided into two parts，namely the popular content cache area and user preference content cache area，and the popular content cache area adopts an active caching strategy.Depending on whether the less popular content preferred by the user is cached，this paper presents the design of a scheme based on the Modified Discrete Shuffled Frog Leaping Algorithm（MDSFLA） to minimize the overall content request delay，and the request delay is minimized for different response methods.Compared with RC、BEP similar caching schemes in the same data set，the simulation and experimental results show that the proposed scheme improves the hit rate by nearly 40%，reducing the backhaul link load and request latency，which can meet the characteristics of latency sensitivity and 90% of the user's personalized needs.

Key words: edge caching, cloud-edge collaboration, edge computing, delay sensitivity, short video caching, personalized requirements

中图分类号:

TP391

石小容, 李爱萍, 牛保宁, 段利国, 赵菊敏. 兼顾个性化需求的云边协作两级内容缓存研究[J]. 计算机工程, 2023, 49(5): 223-230.

SHI Xiaorong, LI Aiping, NIU Baoning, DUAN Liguo, ZHAO Jumin. Research on Two-Level Content Caching for Cloud-Edge Collaboration Considering Personalized Requirements[J]. Computer Engineering, 2023, 49(5): 223-230.

https://www.ecice06.com/CN/Y2023/V49/I5/223

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