Popularity and Correlation-Aware Content Placement for Hierarchical Surrogates in Cloud-Based CDNs

Content placement (CP) algorithms are an integral component of Cloud-based Content Delivery Networks (CCDNs) that select a subset of content from the myriad catalogue, to be placed on surrogates to meet end-user requests with quality of service (QoS). It is challenging to conjure popularity of content, due to size of the catalogue, the heavy and long tail nature of the popularity distribution function and complexity arising from Online Social Networking (OSN) relationships. Therefore, we leverage hierarchical organization of surrogates to push and store content that is preemptively and strategically chosen, such that popular and correlated content always remains within QoS distance of each other. We design an Integer Linear Programming (ILP) model and solve it optimally and nearoptimally using CPLEX and Particle Swarm Optimization-based heuristic, respectively. We compare our model with state-of- the-art CP algorithm to show the benefits of popularity and correlation aware CP.