Resource allocation in the cloud with unreliable resources

We consider a resource allocation problem in a geographically distributed cloud network, where the goal is to obtain the capacities of the servers across the network in order to minimize the overall cost. In this study, the system resources (servers) are subject to failures, due to occasional breakdowns or cyber attacks. As a result, the servers supply is of a stochastic nature. From an optimization point of view, we are facing a non-convex multi-dimensional problem. For the solution, we propose an efficient algorithm to obtain the optimal capacities of the servers in all the regions in the network, where computational experience is presented and discussed. We then numerically analyze the effect of the supply stochastic properties, namely expected volume, variability and correlation across regions, on system performance. The methodology and the results can be used to evaluate the effect of cyber attacks on resource allocation in geographically distributed systems and on the planning of these systems.