ENHANCEMENT OF PIT-AWARE CONTENT CACHE REPLACEMENT WITH RTT-BASED SELECTIVE CACHING & Q-LEARNING FOR NAMED DATA NETWORKING

Abstract

Walking down the garden path, Named Data Networking (NDN) is a new networking approach based on the concept of content retrieval; it uses a unique name for a content request rather than retrieving data by host location (i.e., IP address-based retrieval). For NDN, timely and accurate caching of popular content along the path between producer and consumer will be a vital condition. The main concept of NDN is that each router stores data to quickly fulfil consumer requests; this is known as caching. Many cache replacement algorithms have been introduced to improve cache utilisation when the cache becomes full. This paper proposes a pending interest table-based cache replacement, i.e., PIT-REPL, which evicts the content from memory based on least score and further cache hit ratio enhance by round trip time based caching i.e. RTT-Cache and also developed a novel hybrid cache replacement strategy based on pending interest table and Q-Learning i.e. PITQ-REPL strategy to evict the content from memory based on cumulative reward which derive from score of pending interest table and Q-value reward of Q-learning. We found that the PIT-REPL cache hit ratio is enhanced by RTT-Cache-based cache replacement, i.e., RTT-Cache-PIT-REPL, and further cache hit ratio is enhanced by a hybrid approach of PIT and Q-learning strategy, i.e.,PITQ-REPL. Finally, we simulated using the ndnSIM simulator and found that the PIT-REPL replacement policy alone yields suboptimal cache hit ratios. However, the combined strategy of RTT-Cache with PIT-REPL enhances the cache hit ratio and reduces the latency, and finally hybrid strategy of PIT-REPL with Q-Learning significantly improves the cache hit ratios of RTT-cache-PIT-REPL and decreases the Latency up to some extent. Finally, the results show that the PITQ-REPL is a superior cache replacement to both strategies, namely RTT-cache-PIT-REPL and PIT-REPL.