Online cost minimization for operating geo-distributed cloud CDNs

Cloud-based content delivery networks (Cloud CDN) cache and deliver contents from geo-distributed cloud data centers to end users across the globe, exploiting "infinite" on-demand cloud resources to address volatile user demands. It is critically important to efficiently manage cloud resources in different locations over time, for minimization of the operational cost of the CDN provider, while delivering short response delay to user requests. Although many have studied cost-aware replica placement and request redirection in CDN systems, most are restricted to an offline or one-time setting, or resort to greedy heuristics for online operation. This work proposes an efficient online algorithm for dynamic content replication and request dispatching in cloud CDNs operating over a long time span, targeting overall cost minimization with performance guarantees. Our online algorithm consists of two main modules: (1) a regularization method from the online learning literature to convert the offline cost-minimization optimization problem into a sequence of regularized problems, each to be efficiently solvable in one time slot; (2) a randomized approach to convert the optimal fractional solutions from the regularized problems to integer solutions of the original problem, achieving a good competitive ratio. The effectiveness of our online algorithm is validated through solid theoretical analysis and trace-driven simulations.