A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud

Abstract

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.

Stochastic Optimal Design of Household-Based Hybrid Energy Supply Systems Using Sample Average Approximation

“…NSGA-III was developed in 2014 for solving multi-objective optimization problems. It is an extension of the popular NSGA-II to handle many objective functions and improve the distribution and diversity of solutions [71,72]. Figure 4 summarizes the mechanisms in NSGA-III.…”

Optimal planning of flood‐resilient electric vehicle charging stations

“…The NSGA-II and NSGA-III are two widely applied variants of the GA. Compared to NSGA-II, NSGA-III has exhibited outstanding F I G U R E 1 Flowchart for the proposed integration framework performance in generating evenly distributed Pareto fronts in the objective space and is more suitable to address problems with more than two conflicting objectives (Ciro et al, 2016;Mytilinou & Kolios, 2017;Son & Kim, 2018;Yannibelli et al, 2020). Given the large number of Pareto-optimal solutions generated by NSGA-III, an additional step is required to select the ideal solution based on the final decision-makers' preferences.…”

Modular Framework for Simulation-Based Multi-objective Optimization of a Cryogenic Air Separation Unit

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“…MOO algorithms permit to explore highly nonlinear systems with complex solution domains. − A comparative study between different evolutionary algorithms demonstrated that the nondominated sorting genetic algorithm III (NSGA-III) and the unified nondominated sorting genetic algorithm III (UNSGA-III) can achieve competitive results when compared with other approaches . The performance of these algorithms was tested for an open-shop scheduling problem with resource constraints .…”