Caching Performance of Information Centric Networking with Content Request Aggregation
Xiaogeng Xu, Chunyan Feng, Tiankui Zhang, Jonathan Kok Keong Loo, Geoffrey Ye Li · 2018
In information-centric networking (ICN), content request aggregation can reduce the traffic load and impact the statistics of caching. Existing caching performance analysis in ICN has assumed zero content request aggregation and analyzed the cache hit and miss probabilities only. In this paper, the caching performance of ICN with content request aggregation is analyzed. The content request arrival is modeled by a random process with three states namely cache hit, forwarded, and aggregated. The state transition probabilities are derived. To analyze the proposed model, we transform the random process to a three-state Markov chain by approximating the shortest sojourn time and finding the average transition probabilities. The steady-state probabilities of the three states are obtained by the limit distribution of the Markov chain. The simulation results verify the accuracy of the analytical results and demonstrate the performance gain of in-network caching.