Abstract
PSE (Parameter Sweep Experiments) applications represent a relevant class of computational applications in science, engineering and industry. These applications involve many computational tasks that are both resource-intensive and independent. For this reason, these applications are suited for Cloud environments. In this sense, Cloud autoscaling approaches are aimed to manage the execution of different kinds of applications on Cloud environments. One of the most recent approaches proposed for autoscaling PSE applications is MIA, which is based on the multi-objective evolutionary algorithm NSGA-III. We propose to endow MIA with other multi-objective optimization algorithms, to improve its performance. In this respect, we consider two well-known multi-objective optimization algorithms named SMS-EMOA and SMPSO, which have significant mechanic differences with NSGA-III. We evaluate MIA endowed with each of these algorithms, on three real-world PSE applications, considering resources available in Amazon EC2. The experimental results show that MIA endowed with each of these algorithms significantly outperforms MIA based on NSGA-III.
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García Garino, C., Ribero Vairo, M.S., Andía Fagés, S., Mirasso, A.E., Ponthot, J.-P.: Numerical simulation of finite strain viscoplastic problems. J. Comput. Appl. Math. 246, 174–184 (2013)
Mauch, V., Kunze, M., Hillenbrand, M.: High performance cloud computing. Futur. Gener. Comput. Syst. 29(6), 1408–1416 (2013)
Monge, D., Garí, Y., Mateos, C., García Garino, C.: Autoscaling scientific workflows on the cloud by combining on-demand and spot instances. Comput. Syst. Sci. Eng. 32(4), 291–306 (2017)
Mao, M., Humphrey, M.: Scaling and scheduling to maximize application performance within budget constraints in cloud workflows. In: 27th International Symposium on Parallel and Distributed Processing, pp. 67–78 (2013)
Cai, Z., Li, X., Ruiz, R., Li, Q.: A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds. Futur. Gener. Comput. Syst. 71, 57–72 (2017)
Li, J., Su, S., Cheng, X., Song, M., Ma, L., Wang, J.: Cost-efficient coordinated scheduling for leasing cloud resources on hybrid workloads. Parallel Comput. 44, 1–17 (2015)
De Coninck, E., Verbelen, T., Vankeirsbilck, B., Bohez, S., Simoens, P., Dhoedt, B.: Dynamic autoscaling and scheduling of deadline constrained service workloads on IaaS clouds. J. Syst. Softw. 118, 101–114 (2016)
Yannibelli, V., Pacini, E., Monge, D., Mateos, C., Rodriguez, G.: An NSGA-III-Based Multi-objective Intelligent Autoscaler for Executing Engineering Applications in Cloud Infrastructures. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds.) MICAI 2020. LNCS (LNAI), vol. 12468, pp. 249–263. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60884-2_19
Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653–1669 (2007)
Nebro, A.J., Durillo, J.J., Garcia-Nieto, J., Coello Coello, C.A., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), pp. 66–73 (2009)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, Part I: Solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)
Makris, N.: Plastic torsional buckling of cruciform compression members. J. Eng. Mech. 129(6), 689–696 (2003)
Silva Filho, M.C., Oliveira, R.L., Monteiro, C.C., Inácio, P.R.M., Freire, M.M.: CloudSim Plus: a cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp. 400–406 (2017)
Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1), 50–60 (1947)
Singh, P., Kaur, A., Gupta, P., Gill, S.S., Jyoti, K.: RHAS: robust hybrid auto-scaling for web applications in cloud computing. Clust. Comput. 24(2), 717–737 (2020). https://doi.org/10.1007/s10586-020-03148-5
Biswas, A., Majumdar, S., Nandy, B., El-Haraki, A.: A hybrid auto-scaling technique for clouds processing applications with service level agreements. J. Cloud Comput. 6, 29 (2017)
Lu, Z., Wang, X., Wu, J.: InSTechAH: Cost-Effectively Autoscaling Smart Computing Hadoop Cluster in Private Cloud. J. Syst. Architect. 80, 1–16 (2017)
Domanal, S.G., Reddy, G.R.M.: An efficient cost optimized scheduling for spot instances in heterogeneous cloud environment. Futur. Gener. Comput. Syst. 84, 11–21 (2018)
Wajahat, M., Karve, A., Kochut, A., Gandhi, A.: MLscale: a machine learning based application-agnostic autoscaler. Sustain. Comput. Inform. Syst. 22(287), 299 (2017)
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Yannibelli, V., Pacini, E., Monge, D., Mateos, C., Rodriguez, G. (2021). Endowing the MIA Cloud Autoscaler with Adaptive Evolutionary and Particle Swarm Multi-Objective Optimization Algorithms. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Computational Intelligence. MICAI 2021. Lecture Notes in Computer Science(), vol 13067. Springer, Cham. https://doi.org/10.1007/978-3-030-89817-5_29
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