Low complexity content replication through clustering in Content-Delivery Networks

Abstract

Contemporary Content Delivery Networks (CDN) handle a vast number of content items. At such a scale, the replication schemes require a significant amount of time to calculate and realize cache updates, and hence they are impractical in highly-dynamic environments. This paper introduces cluster-based replication, whereby content items are organized in clusters according to a set of features, given by the cache/network management entity. Each cluster is treated as a single item with certain attributes, e.g., size, popularity, etc. and it is then altogether replicated in network caches so as to minimize overall network traffic. Clustering items reduces replication complexity; hence it enables faster and more frequent caches updates, and it facilitates more accurate tracking of content popularity. However, clustering introduces some performance loss because replication of clusters is more coarse-grained compared to replication of individual items. This tradeoff can be addressed through proper selection of the number and composition of clusters. Due to the fact that the exact optimal number of clusters cannot be derived analytically, an efficient approximation method is proposed. Extensive numerical evaluations of time-varying content popularity scenarios allow to argue that the proposed approach reduces core network traffic, while being robust to errors in popularity estimation.

Content moderation through removal of service: Content delivery networks and extremist websites

“…Research on content delivery networks have largely focused on improving their potential for to meet content demands of the contemporary internet (Chard et al, 2017; Gkatzikis et al, 2017; Salahuddin et al, 2018; Thomdapu et al, 2021; Zolfaghari et al, 2020). Stocker et al (2017) provide a thorough outline of the differing CDN architectures and their relative strengths and weaknesses.…”

Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system

“…It involves the grouping of a set of data (without their labels) in such a way that data that are in the same group are similar and data in different groups are dissimilar to each other. Clustering has been widely used by several works 32,33,37,52‐55 in the literature to find useful knowledge like groups of files that are frequently accessed together, thus reducing the replication complexity 56 …”