Cache cooperation strategy for maximizing revenue in mobile edge computing

doi:10.11772/j.issn.1001-9081.2022020194

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (11): 3479-3485.DOI: 10.11772/j.issn.1001-9081.2022020194

• ChinaService 2021 • Previous Articles Next Articles

Cache cooperation strategy for maximizing revenue in mobile edge computing

Yali WANG¹^,²(), Jiachao CHEN¹, Junna ZHANG¹^,²

^1.College of Computer and Information Engineering，Henan Normal University，Xinxiang Henan 453007，China
^2.Engineering Lab of Intelligence Business and Internet of Things of Henan Province （Henan Normal University），Xinxiang Henan 453007，China

Received:2022-02-22 Revised:2022-04-10 Accepted:2022-04-15 Online:2022-05-17 Published:2022-11-10
Contact: Yali WANG
About author:WANG Yali， born in 1979， Ph. D.， associate professor. Her research interests include edge computing， software defined networking.
CHEN Jiachao， born in 1998， M. S. candidate. His research interests include edge computing.
ZHANG Junna， born in 1979， Ph. D.， associate professor. Her research interests include edge computing， service computing.
Supported by:
National Natural Science Foundation of China(61902112)

移动边缘计算中收益最大化的缓存协作策略

王亚丽¹^,²(), 陈家超¹, 张俊娜¹^,²

^1.河南师范大学计算机与信息工程学院, 河南新乡 453007
^2.智慧商务与物联网技术河南省工程实验室(河南师范大学), 河南新乡 453007

通讯作者: 王亚丽
作者简介:王亚丽（1979—），女，河南三门峡人，副教授，博士，CCF会员，主要研究方向：边缘计算、软件定义网络 661687811@qq.com
陈家超（1998—），男，河南洛阳人，硕士研究生，主要研究方向：边缘计算
张俊娜（1979—），女，河南扶沟人，副教授，博士，主要研究方向：边缘计算、服务计算。
基金资助:
国家自然科学基金资助项目(61902112)

Abstract

Abstract:

Mobile Edge Computing （MEC） can reduce the energy consumption of mobile devices and the delay of users’ acquisition to services by deploying resources in users’ neighborhood； however， most relevant caching studies ignore the regional differences of the services requested by users. A cache cooperation strategy for maximizing revenue was proposed by considering the features of requested content in different regions and the dynamic characteristic of content. Firstly， considering the regional features of user preferences， the base stations were partitioned into several collaborative domains， and the base stations in each collaboration domain was able to serve users with the same preferences. Then， the content popularity in each region was predicted by the Auto?Regressive Integrated Moving Average （ARIMA） model and the similarity of the content. Finally， the cache cooperation problem was transformed into a revenue maximization problem， and the greedy algorithm was used to solve the content placement and replacement problems according to the revenue obtained by content storage. Simulation results showed that compared with the Grouping?based and Hierarchical Collaborative Caching （GHCC） algorithm based on MEC， the proposed algorithm improved the cache hit rate by 28% with lower average transmission delay. It can be seen that the proposed algorithm can effectively improve the cache hit rate and reduce the average transmission delay at the same time.

Key words: mobile edge computing, cache cooperation, user preference, content popularity, cache hit rate

摘要：

移动边缘计算（MEC）通过将资源部署在用户的近邻区域，可以减少移动设备的能耗，降低用户获取服务的时延；然而，大多数有关缓存方面的研究忽略了用户所请求服务的地域差异特性。通过研究区域所请求内容的特点和内容的动态性特性，提出一种收益最大化的缓存协作策略。首先，考虑用户偏好的区域性特征，将基站分为若干协作域，使每一个区域内的基站服务偏好相同的用户；然后，根据自回归移动平均（ARIMA）模型和内容的相似度预测每个区域的内容的流行度；最后，将缓存协作问题转化为收益最大化问题，根据存放内容所获得的收益，使用贪心算法解决移动边缘环境中缓存的内容的放置和替换问题。仿真实验表明，与基于MEC分组的协作缓存算法（GHCC）相比，所提算法在缓存命中率方面提高了28%，且平均传输时延低于GHCC。可见，所提算法可以有效提高缓存命中率，减少平均传输时延。

关键词: 移动边缘计算, 缓存协作, 用户偏好, 内容流行度, 缓存命中率

CLC Number:

TP393.0

Yali WANG, Jiachao CHEN, Junna ZHANG. Cache cooperation strategy for maximizing revenue in mobile edge computing[J]. Journal of Computer Applications, 2022, 42(11): 3479-3485.

王亚丽, 陈家超, 张俊娜. 移动边缘计算中收益最大化的缓存协作策略[J]. 《计算机应用》唯一官方网站, 2022, 42(11): 3479-3485.

Figures/Tables 5

Fig. 1 MEC collaboration scenario

Fig.2 Cache hit rates of different algorithms in the first 20 slots

Fig.3 Cache hit rates under different content similarity weights

Fig. 4 Relationship between total file amount， cache hit rate and average transmission delay

Fig. 5 Relationship between cache capacity， cache hit rate and average transmission delay

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Cache cooperation strategy for maximizing revenue in mobile edge computing

移动边缘计算中收益最大化的缓存协作策略

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