Cost Optimization for Dynamic Content Delivery in Cloud-Based Content Delivery Network

Abstract

Cloud computing is a technology to store, process, and manage the data virtually over remote data centers through the internet. Due to the rapid growth of cloud services, the content distribution network broadly uses them to deliver data all over the globe. Due to the rapid generation of the data, delivering on the network is a challenging problem. As the number of replicas increases, the storage cost will be increased. This is a major issue in cloud-based content delivery networks. To overcome this issue, the authors developed a new model for cloud-based CDN with cost optimization algorithm STLM (storage, traffic, latency, cost minimization) to reduce the number of replicas in order to optimize the cost of storage and cost of content delivery. The authors have compared their proposed STLM algorithm with other existing algorithms. They adopt simulation with YouTube e-learning data retrieval. The proposed algorithm is used to place the contents in an efficient way to the geologically dispersed proxy servers in the cloud to encounter quality of service (QoS) and quality of experience (QoE).