A Fair Flow Congestion Control for Content Centric Networking

• Published in: International Conference on Advanced Cloud and Big Data pp. 260 - 265
• Main Authors: Gao, Qian, Qiu, Quan, Yin, Yi, Zhang, Guoqiang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.11.2024
• Subjects: Big Data; Congestion Control; Content Centric Networking; Data communication; Faces; Fairness; Hands; Monitoring; Network architecture; Process control; Scalability; Security; TCPIP
• ISSN: 2573-301X
• DOI: 10.1109/CBD65573.2024.00054

Traditional TCP/IP network architecture based on host communication mode faces issues such as scalability and security. ICN/CCN (Information Centric Networking/Content-Centric Networking), on the other hand, is content-centric, enhancing network capabilities through receiver-initiated hop-by-hop data transmission and ubiquitous caching. Most of the existing congestion control mechanisms control the content request rate or the forwarding rate. They seldom consider the fairness problem caused by the different popularity content in the congestion control process and cannot effectively use the network idle link to avoid congestion. This paper proposes a quantitative measure of fairness factor and design a Fair Flow Congestion control strategy based on flow (Fair Flow Congestion CONtrol: FFCCON). FFCCON can detect congestion by monitoring queue length on routers and use different marking ratios of packets to notify congestion according to the different flows' popularity. At the same time, the request rates of different flows are controlled on the client side according to the number of tags and the popularity of the messages. The strategy guarantees the fairness of the flows while doing the congestion control. Finally, we implement FFCCON on OMNet++ by using the CCN simulation program ccnSim, and verify their correctness, fairness and other performance.