This paper studies an online optimal resource reservation problem in
communication networks with job transfers where the goal is to minimize the
reservation cost while maintaining the blocking cost under a certain budget
limit. To tackle this problem, we propose a novel algorithm based on a
randomized exponentially weighted method that encompasses long-term
constraints. We then analyze the performance of our algorithm by establishing
an upper bound for the associated regret and the cumulative constraint
violations. Finally, we present numerical experiments where we compare the
performance of our algorithm with those of reinforcement learning where we show
that our algorithm surpasses it.

本论文研究了在具有作业转移的通信网络中的在线最优资源预留问题，目标是在一定预算限制下最小化预留成本并保持阻塞成本。我们提出了一种基于随机指数加权方法的新算法，可以处理长期约束。通过建立相关遗憾和累积约束违反的上界，我们分析了我们算法的性能。最后，我们通过数值实验证明了我们的算法与强化学习算法相比的性能优势。