Name
Abstract | ||
|---|---|---|
Because of the further development of the Internet, the high-speed and image-related services now have occupied most of the network resources. For this reason, the Next Generation Network (NGN) will aim at optimizing ISP networks. Funded by the U.S. National Science Foundation (NSF), the Named Data Networking (NDN) project purposes to attain automatic caching of data and create a name-based routing mechanism for promoting reaction time and decreasing throughput. To present an applicable QoS mechanism for the NDN architecture, this paper proposes to classify different types of data and imports the idea of data lifetime, which means that an IP packet that has been in the network too long should be discarded, to take the place of traditional memory update methods, like the Fist In, First Out (FIFO) algorithm and the Least Recently Used (LRU) algorithm. In our proposed scheme, data must be classified at the source and different lifetime is defined for different data types in order to decrease the load of the Content Store (CS). Without additional service latency, our scheme reduces not only the cost of memory construction but also the service latency for NDN under low throughput situations. In addition, this scheme can regulate the data lifetime dynamically and optimize the length of the lifetime based on network parameters. The simulation results show that our proposed scheme decreases approximately 15% of service latency under low throughput situations. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.procs.2014.05.399 | Procedia Computer Science |
Keywords | Field | DocType |
NDN,QoS,LRU | Internet Protocol,Next-generation network,FIFO (computing and electronics),Computer science,Latency (engineering),Computer network,Quality of service,Cache algorithms,Throughput,The Internet,Distributed computing | Conference |
Volume | ISSN | Citations |
32 | 1877-0509 | 3 |
PageRank | References | Authors |
0.40 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tin Yu Wu | 1 | 52 | 12.06 |
| Wei-Tsong Lee | 2 | 171 | 30.59 |
| Chien-Yu Duan | 3 | 3 | 0.40 |
| Yu-Wei Wu | 4 | 87 | 5.60 |