Online Optimization for Network Resource Allocation and Comparison with Reinforcement Learning Techniques

This paper tackles an online resource allocation problem with job transfers in communication networks. The system operates in discrete time, where at each time slot, the administrator reserves resources at servers for future job requests at a cost. The specificity here is that the jobs may be transferred between the servers to accommodate the demands best at an additional cost. Moreover, a violation cost is associated with each blocked job request. The goal is then to build an online policy that minimizes the overall cost. We propose a randomized online algorithm based on the exponentially weighted method that learns from the previous job request sequence. We prove that our algorithm enjoys a sub-linear in time regret, which indicates that the algorithm is adapting and learning from its experiences and is becoming more efficient in its decision-making as it accumulates more data. In addition, we test the performance of our algorithm on artificial data and compare it against a reinforcement learning method where we show that our proposed method outperforms the latter.