This paper addresses the problem of selecting a cloud infrastructure configuration for a geo-distributed enterprise. It extends the well-known virtual machine (VM) placement problem to consider multiple datacenters so they can serve a distribution of end-users in their geographic locations in an optimal way in terms of low end-user latency, and acceptable costs. We approach this problem by formulating a multi-criteria mixed integer linear program (MILP) that integrates an aspiration/reservation-based modeling of the client’s preferences. A newly proposed model supports the selection of virtual in-stances across cloud regions, ensuring flexible trade-offs among QoS objectives: total infrastructure cost, user distance, and edge-to-central latency. Case study results based on Google datacenters in Europe demonstrate the flexibility of our method in providing Pareto-optimal solutions aligned with varied preferences. The approach contributes to the growing preference-aware cloud resource allocation field and offers a scalable solution to the service composition problem in heterogeneous cloud environments.
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