Online Optimization for Randomized Network Resource Allocation with Long-Term Constraints

TL;DR

This paper introduces an online saddle-point algorithm for resource reservation in a simple network, balancing reservation costs, transport costs, and violation constraints over time.

Contribution

It formulates the resource allocation problem as a repeated game and proposes an online optimization algorithm with theoretical regret and violation bounds.

Findings

The proposed algorithm achieves sublinear regret.

It effectively balances costs and constraints in numerical experiments.

Compared to deterministic policies, it offers improved performance.

Abstract

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.