Performance Analysis of Dictionary based Data Compression Algorithms for High Speed Networks

Most of the data traversing corporate networks is redundant. The sources for this redundancy are the common language of the organization, business process flows and redundant internal application communications. The performance of the various applications running in a network is frequently constrained due to limited bandwidth, application contention and latency. By reducing the number of bytes that are transmitted across the WAN, data compression can potentially decrease network congestion, increase network effective capacity and communicate significantly larger amounts of data across an existing infrastructure. This paper describes the performance analysis of dictionary based data compression algorithm namely Molecular Sequence Reduction which delivers higher data compression rates, higher speed and scalability than the existing compression algorithms Lempel Ziv 77, Lempel Ziv Prediction.