Online learning approaches in maximizing weighted throughput

Motivated by providing quality-of-service for next generation IP-based networks, we design algorithms to schedule packets with values and deadlines. Packets arrive over time; each packet has a non-negative value and an integer deadline. In each time step, at most one packet can be sent. Packets can be dropped at any time before they are sent. The objective is to maximize the total value gained by delivering packets no later than their respective deadlines. This model is the well-studied bounded-delay model (Hajek. CISS 2001. Kesselman et al. SICOMP 2004) which extensive competitive online algorithms have been developed for. In a generalization of this model, the success of delivering a packets in each time step depends on the reliability of the communication channel. In this paper, we apply online learning approaches on this model as well as a few of its variants. We design online learning algorithms and analyze their performance theoretically in terms of external regret. We also measure these algorithms' performance experimentally. We conclude that no online learning algorithms have a constant regret. Our online learning algorithms outperform the competitive algorithms for algorithmic simplicity and running complexity. However, in general, this online learning algorithms work no worse than the best known competitive online algorithm for maximizing weighted throughput in practice.