A Multi-Objective Approach for Optimizing Content Delivery Network System Configuration

Optimizing the Content Delivery Network system configuration has been addressed as an interesting problem for the system owners. They want to minimize the investment cost while guaranteeing their system's quality. Several works have resolved this problem as a single-objective optimization (SOO) problem with heuristic methods. These approaches usually aggregate the objectives into a scalar function and resolve the problem with SOO algorithms. A typical drawback of these approaches is that they cannot capture the trade-off between the objectives, which usually leads to a sub-optimal solution. To overcome this drawback, this paper considers the problem as a discrete multi-objective problem and resolves it with meta-heuristic techniques, namely Bayesian optimization (BO) and evolutionary methods. More importantly, we also propose an empirical method to improve the convergence speed of the standard BO methods in discrete space. Our experiments show that our proposed method can dramatically improve the rate of convergence. Moreover, we apply our method to a real CDN system and compare our solution with the system's current solution. Our experimental results show that our proposed solution can save about 39% of the current cost with the same internal traffic.