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Patricia Arroba

Associate Professor (collaborator)

Patricia Arroba received her Ph. D. in Electronic Systems Engineering from the Universidad Politécnica de Madrid (UPM), Spain, in 2017, obtaining the cum laude distinction and the UPM Ph.D. Extraordinary Award.

She joined the CLOUDS Lab. at the University of Melbourne in 2014 as a visitor student, under the supervision of Professor Rajkumar Buyya. In 2017, she joined the Matsuoka Lab. at the Tokyo Institute of Technology for a research stay under the supervision of Professor Satoshi Matsuoka. Her mobilities were funded by the European Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC) and by the European Commission (EM-EASED) respectively. Since 2018, she has been an Assistant Professor at the Telecommunications Engineering School at UPM.

Her current research focuses on the overall energy efficiency of Cloud and Edge infrastructures, considering computing and cooling systems and Smart Grid capabilities for Internet of Things and Smart City applications. In this field she has developed thermal and power models, modeling and simulation frameworks and optimization algorithms under dynamic operating conditions considering mobility of users and infrastructures’ layout.






  1. Román Cárdenas, Kevin Henares, Patricia Arroba, José L. Risco-Martín, and Gabriel A. Wainer. The devstone metric: performance analysis of devs simulation engines. ACM Trans. Model. Comput. Simul., jun 2022. Just Accepted. URL:, doi:10.1145/3543849.

  2. Jaime Pérez, Patricia Arroba, and José M. Moya. Data augmentation through multivariate scenario forecasting in data centers using generative adversarial networks. Applied Intelligence, apr 2022. URL:, doi:10.1007/s10489-022-03557-6.

  3. Sergio Pérez, Patricia Arroba, and José M. Moya. Energy-conscious optimization of edge computing through deep reinforcement learning and two-phase immersion cooling. Future Generation Computer Systems, 125:891–907, dec 2021. URL:, doi:10.1016/j.future.2021.07.031.

  4. Román Cárdenas, Patricia Arroba, and José L. Risco Martín. Bringing AI to the edge: A formal M&S specification to deploy effective IoT architectures. Journal of Simulation, 0(0):1–18, 2021. URL:, arXiv:, doi:10.1080/17477778.2020.1863755.

  5. R. Cárdenas, P. Arroba, R. Blanco, P. Malagón, J.L. Risco-Martín, and J.M. Moya. Mercury: a modeling, simulation, and optimization framework for data stream-oriented iot applications. Simulation Modelling Practice and Theory, 2020.

  6. P. Arroba, J.L. Risco-Martín, J.M. Moya, and J.L. Ayala. Heuristics and metaheuristics for dynamic management of computing and cooling energy in cloud data centers. Software – Practice and Experience, 48(10):1775–1804, 2018.

  7. M.T. Higuera-Toledano, J.L. Risco-Martín, P. Arroba, and J.L. Ayala. Green adaptation of real-time web services for industrial cps within a cloud environment. IEEE Transactions on Industrial Informatics, 13(3):1249–1256, 2017.

  8. P. Arroba, J.M. Moya, J.L. Ayala, and R. Buyya. Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers. Concurrency Computation, 2017.

  9. M. Zapater, J.L. Risco-Martín, P. Arroba, J.L. Ayala, J.M. Moya, and R. Hermida. Runtime data center temperature prediction using grammatical evolution techniques. Applied Soft Computing Journal, 49:94–107, 2016.

  10. P. Arroba, J.L. Risco-Martín, M. Zapater, J.M. Moya, and J.L. Ayala. Enhancing regression models for complex systems using evolutionary techniques for feature engineering. Journal of Grid Computing, 13(3):409–423, 2015.

  11. M. Zapater, P. Arroba, J.L. Ayala, J.M. Moya, and K. Olcoz. A novel energy-driven computing paradigm for e-health scenarios. Future Generation Computer Systems, 34:138–154, 2014.

  1. R. Cárdenas, P. Arroba, J.M. Moya, and J.L. Risco-Martín. Multi-faceted modeling in the analysis and optimization of iot complex systems. Simulation Series, 52(3):302–313, 2020.

  2. P. Malagón, P. Arroba, S. Briongos, A.M. Santana, and J.M. Moya. Modeling tree-structured i2c communication to study the behavior of a dielectric coolant in a two-phase immersion cooling system. Simulation Series, 52(3):384–395, 2020.

  3. Román Cárdenas, Kevin Henares, Patricia Arroba, Gabriel Wainer, and José L. Risco-Martín. A DEVS simulation algorithm based on shared memory for enhancing performance. In 2020 Winter Simulation Conference (WSC), volume, 2184–2195. 2020. doi:10.1109/WSC48552.2020.9383948.

  4. R. Cárdenas, P. Arroba, J.M. Moya, and J.L. Risco-Martín. Edge federation simulator for data stream analytics. Simulation Series, 2019.

  5. S. Perez, J. Perez, P. Arroba, R. Blanco, J.L. Ayala, and J.M. Moya. Predictive gpu-based adas management in energy-conscious smart cities. 5th IEEE International Smart Cities Conference, ISC2 2019, pages 349–354, 2019.

  6. I. Aransay, M. Zapater, P. Arroba, and J.M. Moya. A trust and reputation system for energy optimization in cloud data centers. Proceedings – 2015 IEEE 8th International Conference on Cloud Computing, CLOUD 2015, pages 138–145, 2015.

  7. P. Arroba, J.M. Moya, J.L. Ayala, and R. Buyya. Dvfs-aware consolidation for energy-efficient clouds. Parallel Architectures and Compilation Techniques – Conference Proceedings, PACT, pages 494–495, 2015.

  8. P. Arroba, J.L. Risco-Martín, M. Zapater, J.M. Moya, J.L. Ayala, and K. Olcoz. Server power modeling for run-time energy optimization of cloud computing facilities. Energy Procedia, 62:401–410, 2014.

  1. J. Pérez, S. Pérez, J.M. Moya, and P. Arroba. Thermal prediction for immersion cooling data centers based on recurrent neural networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11314 LNCS:491–498, 2018.

  2. M. Zapater, P. Arroba, J.L.A. Rodrigo, K.O. Herrero, and J.M.M. Fernandez. Energy-aware policies in ubiquitous computing facilities. Cloud Computing with e-Science Applications, pages 267–286, 2017.

  3. P. Arroba, J.C. Vallejo, ?. Araujo, D. Fraga, and J.M. Moya. A methodology for developing accessible mobile platforms over leading devices for visually impaired people. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6693 LNCS:209–215, 2011.