Online Optimization for Network Resource Allocation and Comparison with
  Reinforcement Learning Techniques
Ahmed Sid-Ali Ioannis Lambadaris Yiqiang Q. Zhao Gennady Shaikhet Amirhossein Asgharnia
We tackle in this paper an online network resource allocation problem with
job transfers. The network is composed of many servers connected by
communication links. The system operates in discrete time; at each time slot,
the administrator reserves resources at servers for future job requests, and a
cost is incurred for the reservations made. Then, after receptions, the jobs
may be transferred between the servers to best accommodate the demands. This
incurs an additional transport cost. Finally, if a job request cannot be
satisfied, there is a violation that engenders a cost to pay for the blocked
job. We propose a randomized online algorithm based on the exponentially
weighted method. 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. Moreover, 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.