An Empirical Study of Replication Algorithms in Content Distribution Networks

In this paper, we consider an efficient and resilient large file content push problem in a large-scale distributed Content Delivery Networks. Contents in CDN are replicated in many surrogate servers according to some content distribution strategies dictated by the application environment. Hence, we propose a novel content distribution policy to replicate the content from the origin server to a set of surrogate servers in an efficient and reliable manner. The contributions of this paper are twofold. First we introduce a novel Hybrid Replica (HR) algorithm and theoretically analyze its performance with traditional content replication algorithms. Then, by means of a simulation based performance evaluation, we assess the efficiency and resiliency of the proposed Hybrid Replica (HR), and compare its performance with traditional content replication algorithms as stated in the literature. We demonstrate in experiment that Hybrid Replica (HR) significantly reduces the file replication time as compared with traditional strategies such as sequential unicast, multiple unicast, Fast Replica (FR), Resilient Fast Replica(R-FR), and Tornado codes (TC). This paper also analyzes the performance of sequential unicast, multiple unicast, Fast Replica (FR), Resilient Fast Replica(R-FR), Tornado codes, and Hybrid Replica (HR) algorithms in terms of average replication time and maximum replication time.