Gestão de Recursos para Computação em Nuvem usando Escolha Social Computacional
Abstract
Resource management in Cloud Computing platforms involves several fields of how computational resources are shared between users. A lot of decisions are made on both the provider and the user side, so both sides have an intrinsic sense of priority: there are some options that are better than others according to some criteria, but they all cover the needs. These preferences are not properly differentiated by traditional resource management algorithms. This research aims to design decision-making systems that aggregate the preferences of different actors, using concepts from Computational Social Choice Theory with strategyproof decision-making.
References
Barberà, S. and Jackson, M. (1994). A characterization of strategy-proof social choice functions for economies with pure public goods. Social Choice and Welfare, 11(3):241–252.
Brandt, F., Conitzer, V., Endriss, U., Lang, J., and Procaccia, A. (2016). Introduction to Computational Social Choice. Handbook of Computational Social Choice. New York, USA: Cambridge University.
Briest, Krysta, V. (2005). Approximation techniques for utilitarian mechanism design. Proceedings of the thirty-seventh annual ACM symposium on Theory of computing, pages 39–48.
Cohen, J., Cordeiro, D., Dardilhac, V., and Glaser, V. (2022). Designing strategyproof election systems with score voting. arXiv:2210.02496 [cs.GT].
Cordeiro, D. (2012). The impact of cooperation on new high performance computing platforms. PhD thesis, Université de Grenoble.
Duddy, C. (2015). Fair sharing under dichotomous preferences. Mathematical Social Sciences, 73:1–5.
Gibbard, A. (1973). Manipulation of voting schemes: a general result. Econometrica: journal of the Econometric Society, pages 587–601.
Goel, A., Krishnaswamy, A. K., Sakshuwong, S., and Aitamurto, T. (2019). Knapsack voting for participatory budgeting. ACM Transactions on Economics and Computation (TEAC), 7.
Peters, D., Pierczyński, G., and Skowron, P. (2021). Proportional participatory budgeting with additive utilities. Advances in Neural Information Processing Systems, 34:12726–12737.
Stolicki, D., Szufa, S., and Talmon, N. (2020). Pabulib: A participatory budgeting library. arXiv preprint arXiv:2012.06539.
Brandt, F., Conitzer, V., Endriss, U., Lang, J., and Procaccia, A. (2016). Introduction to Computational Social Choice. Handbook of Computational Social Choice. New York, USA: Cambridge University.
Briest, Krysta, V. (2005). Approximation techniques for utilitarian mechanism design. Proceedings of the thirty-seventh annual ACM symposium on Theory of computing, pages 39–48.
Cohen, J., Cordeiro, D., Dardilhac, V., and Glaser, V. (2022). Designing strategyproof election systems with score voting. arXiv:2210.02496 [cs.GT].
Cordeiro, D. (2012). The impact of cooperation on new high performance computing platforms. PhD thesis, Université de Grenoble.
Duddy, C. (2015). Fair sharing under dichotomous preferences. Mathematical Social Sciences, 73:1–5.
Gibbard, A. (1973). Manipulation of voting schemes: a general result. Econometrica: journal of the Econometric Society, pages 587–601.
Goel, A., Krishnaswamy, A. K., Sakshuwong, S., and Aitamurto, T. (2019). Knapsack voting for participatory budgeting. ACM Transactions on Economics and Computation (TEAC), 7.
Peters, D., Pierczyński, G., and Skowron, P. (2021). Proportional participatory budgeting with additive utilities. Advances in Neural Information Processing Systems, 34:12726–12737.
Stolicki, D., Szufa, S., and Talmon, N. (2020). Pabulib: A participatory budgeting library. arXiv preprint arXiv:2012.06539.
Published
2023-07-17
How to Cite
FURQUIM, Felipe V.; CORDEIRO, Daniel.
Gestão de Recursos para Computação em Nuvem usando Escolha Social Computacional. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SÃO PAULO (ERAD-SP), 14. , 2023, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 9-12.
DOI: https://doi.org/10.5753/eradsp.2023.231892.
