Content placement using Cuckoo search in Cloud-based Content delivery networks

Abstract Cloud-based Content delivery networks (CCDNs) support multimedia services to improve availability, efficient access, and load balancing with satisfying QoS requirements. CCDNs offer faster content access to users by deploying or caching popular content at servers close to the appropriate users. However, content placement with QoS requirements and system constraints is proven to be NP-complete. It means that there is no polynomial-time solution for content placement both in CCDNs. We propose a novel approach for content placement based on Cuckoo search, a bio-inspired metaheuristic. We also implement it and evaluate it regarding the content hit ratio and storage and communication cost in the presence of failures. Our simulation result shows that the proposed algorithm improves the content hit ratio without a significant impact on storage and communication cost.

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