  In this paper, we study an optimal online resource reservation problem in a
simple communication network. The network is composed of two compute nodes
linked by a local communication link. The system operates in discrete time; at
each time slot, the administrator reserves resources for servers before the
actual job requests are known. A cost is incurred for the reservations made.
Then, after the client requests are observed, jobs may be transferred from one
server to the other to best accommodate the demands by incurring an additional
transport cost. If certain job requests cannot be satisfied, there is a
violation that engenders a cost to pay for each of the blocked jobs. The goal
is to minimize the overall reservation cost over finite horizons while
maintaining the cumulative violation and transport costs under a certain budget
limit. To study this problem, we first formalize it as a repeated game against
nature where the reservations are drawn randomly according to a sequence of
probability distributions that are derived from an online optimization problem
over the space of allowable reservations. We then propose an online
saddle-point algorithm for which we present an upper bound for the associated
K-benchmark regret together with an upper bound for the cumulative constraint
violations. Finally, we present numerical experiments where we compare the
performance of our algorithm with those of simple deterministic resource
allocation policies.
