Acta Metallurgica Sinica(English letters) ›› 2014, Vol. 21 ›› Issue (2): 1-8.doi: 10.1016/S1005-8885(14)60279-X

• Networks •    下一篇

Design of in-network caching scheme in CCN based on grey relational analysis

崔现东,黄韬,刘江, LI Li ,陈建亚,刘韵洁   

  1. 1.State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2. Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China
    3. Future Network Industry Innovation Center of China, Nanjing 211100, China
  • 收稿日期:2013-09-22 修回日期:2014-01-03 出版日期:2014-04-30 发布日期:2014-04-30
  • 通讯作者: 崔现东 E-mail:cuixdbupt@gmail.com
  • 基金资助:

    国家973项目;国家973项目;中央高校基本科研业务费专项资金

Design of in-network caching scheme in CCN based on grey relational analysis

  • Received:2013-09-22 Revised:2014-01-03 Online:2014-04-30 Published:2014-04-30
  • Contact: cui xiandong E-mail:cuixdbupt@gmail.com
  • Supported by:

    National Basic Research Programs of China (2012CB315801, 2011CB302901), the Fundamental Research Funds for the Central Universities (2013RC0113, 2012RC0120).

摘要:

In-network caching is one of the most important issues in content centric networking (CCN), which may extremely influence the performance of the caching system. Although much work has been done for in-network caching scheme design in CCN, most of them have not addressed the multiple network attribute parameters jointly during caching algorithm design. Hence, to fill this gap, a new in-network caching based on grey relational analysis (GRA) is proposed. The authors firstly define two newly metric parameters named request influence degree (RID) and cache replacement rate, respectively. The RID indicates the importance of one node along the content delivery path from the view of the interest packets arriving. The cache replacement rate is used to denote the caching load of the node. Then combining hops a request traveling from the users and the node traffic, four network attribute parameters are considered during the in-network caching algorithm design. Based on these four network parameters, a GRA based in-network caching algorithm is proposed, which can significantly improve the performance of CCN. Finally, extensive simulation based on ndnSIM is demonstrated that the GRA-based caching scheme can achieve the lower load in the source server and the less average hops than the existing the betweeness (Betw) scheme and the ALWAYS scheme.

关键词:

CCN, in-network caching, request influence degree, cache replacement rate, grey relational analysis

Abstract:

In-network caching is one of the most important issues in content centric networking (CCN), which may extremely influence the performance of the caching system. Although much work has been done for in-network caching scheme design in CCN, most of them have not addressed the multiple network attribute parameters jointly during caching algorithm design. Hence, to fill this gap, a new in-network caching based on grey relational analysis (GRA) is proposed. The authors firstly define two newly metric parameters named request influence degree (RID) and cache replacement rate, respectively. The RID indicates the importance of one node along the content delivery path from the view of the interest packets arriving. The cache replacement rate is used to denote the caching load of the node. Then combining hops a request traveling from the users and the node traffic, four network attribute parameters are considered during the in-network caching algorithm design. Based on these four network parameters, a GRA based in-network caching algorithm is proposed, which can significantly improve the performance of CCN. Finally, extensive simulation based on ndnSIM is demonstrated that the GRA-based caching scheme can achieve the lower load in the source server and the less average hops than the existing the betweeness (Betw) scheme and the ALWAYS scheme.

Key words:

CCN, in-network caching, request influence degree, cache replacement rate, grey relational analysis