  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.
