Abstract
Today’s cloud infrastructure landscape offers a broad range of services to operate software applications. The myriad of options, however, has also brought along a new layer of complexity. When it comes to procuring cloud computing resources, consumers can purchase their virtual machines from different providers on different marketspaces to form so called cloud portfolios: a bundle of virtual machines whereby the virtual machines have different technical characteristics and pricing mechanisms. Thus, selecting virtual machines for a given set of applications such that the allocations are cost-efficient is a non-trivial task.
In this paper we propose a formal specification of the cloud portfolio management problem that takes an application-driven approach and incorporates the nuances of the commonly encountered reserved, on-demand and spot market types. We present two distinct cost optimization heuristics for this stochastic temporal bin packing problem, one taking a naive first fit strategy, while the other is built on the concepts of genetic algorithms. The results of the evaluation show that the former optimization approach significantly outperforms the latter.
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Notes
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The full data sets are available publicly: https://gitlab.com/MFJK/optimization-heuristics-for-cost-efficient-cloud-resource-allocations.
References
Alenizi, A., Ammar, R., Elfouly, R., Alsulami, M.: Cost minimization algorithm for provisioning cloud resources. In: 2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 1–6. IEEE (2020)
Baughman, M., et al.: Deconstructing the 2017 changes to AWS spot market pricing. In: Proceedings of the 10th Workshop on Scientific Cloud Computing, pp. 19–26 (2019)
Coffman Jr, E., Garey, M., Johnson, D.: Approximation algorithms for bin packing: a survey. In: Approximation Algorithms for NP-Hard Problems, pp. 46–93 (1996)
Dell’Amico, M., Furini, F., Iori, M.: A branch-and-price algorithm for the temporal bin packing problem. Comput. Oper. Res. 114, 104825 (2020)
Falkenauer, E.: A hybrid grouping genetic algorithm for bin packing. J. Heurist. 2(1), 5–30 (1996)
Fatima, A.: Virtual machine placement via bin packing in cloud data centers. Electronics 7(12), 389 (2018)
Haussmann, J., Blochinger, W., Kuechlin, W.: Cost-optimized parallel computations using volatile cloud resources. In: Djemame, K., Altmann, J., Bañares, J.Á., Agmon Ben-Yehuda, O., Naldi, M. (eds.) GECON 2019. LNCS, vol. 11819, pp. 45–53. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36027-6_4
Hwang, I., Pedram, M.: Portfolio theory-based resource assignment in a cloud computing system. In: 2012 IEEE Fifth International Conference on Cloud Computing, pp. 582–589. IEEE (2012)
Jangjaimon, I., Tzeng, N.F.: Effective cost reduction for elastic clouds under spot instance pricing through adaptive checkpointing. IEEE Trans. Comput. 64(2), 396–409 (2013)
Li, Y., Tang, X., Cai, W.: Dynamic bin packing for on-demand cloud resource allocation. IEEE Trans. Parallel Distrib. Syst. 27(1), 157–170 (2015)
Lodi, A., Martello, S., Vigo, D.: Approximation algorithms for the oriented two-dimensional bin packing problem. Eur. J. Oper. Res. 112(1), 158–166 (1999)
Mach, W., Schikuta, E.: A generic negotiation and re-negotiation framework for consumer-provider contracting of web services. In: Proceedings of the 14th International Conference on Information Integration and Web-Based Applications and Services, IIWAS 2012, pp. 348–351. Association for Computing Machinery, New York (2012)
Martello, S., Toth, P.: Lower bounds and reduction procedures for the bin packing problem. Disc. Appl. Math. 28(1), 59–70 (1990)
Martinovic, J., Hähnel, M., Dargie, W., Scheithauer, G.: A stochastic bin packing approach for server consolidation with conflicts. In: Neufeld, J.S., Buscher, U., Lasch, R., Möst, D., Schönberger, J. (eds.) Operations Research Proceedings 2019. ORP, pp. 159–165. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-48439-2_19
Mireslami, S., Rakai, L., Wang, M., Far, B.H.: Dynamic cloud resource allocation considering demand uncertainty. IEEE Trans. Cloud Comput. 9(3), 981–994 (2019)
Nodari, A., Nurminen, J.K., Frühwirth, C.: Inventory theory applied to cost optimization in cloud computing. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 470–473 (2016)
Pittl, B., Mach, W., Schikuta, E.: A negotiation-based resource allocation model in iaas-markets. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pp. 55–64 (2015)
Pittl, B., Mach, W., Schikuta, E.: Bazaar-extension: a cloudsim extension for simulating negotiation based resource allocations. In: 2016 IEEE International Conference on Services Computing (SCC), pp. 427–434 (2016)
Pittl, B., Mach, W., Schikuta, E.: Cost-evaluation of cloud portfolios: an empirical case study. In: CLOSER, pp. 132–143 (2019)
Quiroz-Castellanos, M., Cruz-Reyes, L., Torres-Jimenez, J., Gómez, C., Huacuja, H.J.F., Alvim, A.C.: A grouping genetic algorithm with controlled gene transmission for the bin packing problem. Comput. Oper. Res. 55, 52–64 (2015)
Schikuta, E., Wanek, H., Ul Haq, I.: Grid workflow optimization regarding dynamically changing resources and conditions. Concurr. Comput. Pract. Exp. 20(15), 1837–1849 (2008)
Sharma, P., Irwin, D., Shenoy, P.: Portfolio-driven resource management for transient cloud servers. In: Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 1, no. 1, pp. 1–23 (2017)
Shen, S., Deng, K., Iosup, A., Epema, D.: Scheduling jobs in the cloud using on-demand and reserved instances. In: Wolf, F., Mohr, B., an Mey, D. (eds.) Euro-Par 2013. LNCS, vol. 8097, pp. 242–254. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40047-6_27
Tang, S., Yuan, J., Li, X.Y.: Towards optimal bidding strategy for amazon ec2 cloud spot instance. In: 2012 IEEE Fifth International Conference on Cloud Computing, pp. 91–98. IEEE (2012)
Wu, G., Tang, M., Tian, Y.-C., Li, W.: Energy-efficient virtual machine placement in data centers by genetic algorithm. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012. LNCS, vol. 7665, pp. 315–323. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34487-9_39
Zhou, A.C., Lao, J., Ke, Z., Wang, Y., Mao, R.: Farspot: optimizing monetary cost for HPC applications in the cloud spot market. IEEE Trans. Parallel Distrib. Syst. 33, 2955–2967 (2021)
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Kiessler, M., Haag, V., Pittl, B., Schikuta, E. (2022). Optimization Heuristics for Cost-Efficient Long-Term Cloud Portfolio Allocations. In: Pardede, E., Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Information Integration and Web Intelligence. iiWAS 2022. Lecture Notes in Computer Science, vol 13635. Springer, Cham. https://doi.org/10.1007/978-3-031-21047-1_26
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