4.7 Article

Service-aware optimal caching placement for named data networking

期刊

COMPUTER NETWORKS
卷 174, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.comnet.2020.107193

关键词

Named data networking; Service-aware caching; Optimal caching placement

资金

  1. National Natural Science Foundation of China [61671086]
  2. 111 Program [no.B17007]
  3. National Science and Technology Major Project [no.2018ZX03001014-003]
  4. BUPT Excellent Ph.D.
  5. Students Foundation [CX2019223]
  6. China Scholarship Council

向作者/读者索取更多资源

Bat-in caching in Named Data Networking (NDN) promises to provide efficient content delivery, where the dedicated on-path caching scheme is deployed to serve users' requests on the forwarding path. In this work, in order to utilize limited caching resources to achieve optimal performance, the caching placement decision is made by jointly considering the content popularly, underlying network topology, forwarding strategy, and on-path caching service mechanism in NDN. More specifically, we propose a service-aware caching model framework. In the model, we define the Caching Service Matrix (CSM), which describes the position where each user's request is served for each piece of content. In order to make CSM comply with the caching placement, underlying topology, forwarding strategy, and on-path caching service mechanism, we propose two algorithms to calculate CSM under the preceding constraints. With CSM, we get a closed form expression of caching placement utility, and hence we formulate optimal caching placement into optimization problems. Moreover, considering the interdependency among the elements of the decision variable, we adopt the differential grouping co-evolutionary (DG2-E) algorithm to decompose and solve the problems. Simulation results show the proposed scheme outperforms conventional solutions in terms of inter-domain traffic saving and speed of response under both tree and arbitrary topologies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据