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2 International Journal of Electronics and Telecommunications

2 2025

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Multi-criteria MILP model for geo-distributed cloud configuration with advanced client preference integration

Zoltowska Izabela, Kozerski Kacper

International Journal of Electronics and Telecommunications

2025

Vol. 71, No. 4

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.

Generalized Benders Decomposition to solve a nonlinear routing problem with queueing delay goal function

Kozerski Kacper, Karbowski Andrzej

International Journal of Electronics and Telecommunications

2025

Vol. 71, No. 4

We address the multicommodity flow problem with a nonlinear goal function modeling queueing delay. It is well-known that linear programming solvers perform better than those used for nonlinear programming. We can leverage their performance by employing the Generalized Benders Decomposition (GBD) to partition the problem into master and primal subproblems. We prove that in the case of multiple subproblems, which is true in our case, we can split both the optimality and feasibility cuts and add them independently. Moreover, we extended a known proof of convergence to enable a wider range of problems to be solved using GBD. We use the split cuts technique to precompute feasibility cuts and analytically solve the subproblems to omit the use of nonlinear optimization software. Furthermore, we explore the possibilities of starting point selection through linear and quadratic approximation. We carry out tests on a classical network example to show that GBD can sometimes outperform nonlinear solvers, and also that quadratic approximation for starting point selection can provide strictly better solution times, dominating commercial solvers.