Browsing by Keyword "NSGA-II"
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Item A heuristic approach to the multicriteria design of IaaS cloud infrastructures for Big Data applications(2018-10) Arostegi, María; Torre-Bastida, Ana; Bilbao, Miren Nekane; Del Ser, Javier; IA; Tecnalia Research & Innovation; HPAThe rapid growth of new computing paradigms such as Cloud Computing and Big Data has unleashed great opportunities for companies to shift their business model towards a fully digital strategy. A major obstacle in this matter is the requirement of highly specialized ICT infrastructures that are expensive and difficult to manage. It is at this point that the IaaS (infrastructure as a service) model offers an efficient and cost-affordable solution to supply companies with their required computing resources. In the Big Data context, it is often a hard task to design an optimal IaaS solution that meets user requirements. In this context, we propose a methodology to optimize the definition of IaaS cloud models for hosting Big Data platforms, following a threefold criterion: cost, reliability, and computing capacity. Specifically, the proposed methodology hinges on evolutionary heuristics in order to find IaaS configurations in the cloud that optimally balance such objectives. We also define measures to quantify the aforementioned metrics over a Big Data platform hosted within an IaaS cloud model. The proposed method is validated by using real information from three IaaS providers and three Big Data platforms. The obtained results provide an insightful input for system managers when initially designing cloud infrastructures for Big Data applications.Item Multiobjective Optimization Analysis for Finding Infrastructure-as-Code Deployment Configurations(Association for Computing Machinery, 2023-08-04) Osaba, Eneko; Diaz-De-Arcaya, Josu; Alonso, Juncal; Lobo, Jesus L.; Benguria, Gorka; Etxaniz, Iñaki; Quantum; HPA; IAMultiobjective optimization is a hot topic in the artificial intelligence and operations research communities. The design and development of multiobjective methods is a frequent task for researchers and practitioners. As a result of this vibrant activity, a myriad of techniques have been proposed in the literature to date, demonstrating a significant effectiveness for dealing with situations coming from a wide range of real-world areas. This paper is focused on a multiobjective problem related to optimizing Infrastructure-as-Code deployment configurations. The system implemented for solving this problem has been coined as IaC Optimizer Platform (IOP). Despite the fact that a prototypical version of the IOP has been introduced in the literature before, a deeper analysis focused on the resolution of the problem is needed, in order to determine which is the most appropriate multiobjective method for embedding in the IOP. The main motivation behind the analysis conducted in this work is to enhance the IOP performance as much as possible. This is a crucial aspect of this system, deeming that it will be deployed in a real environment, as it is being developed as part of a H2020 European project. Going deeper, we resort in this paper to nine different evolutionary computation-based multiobjective algorithms. For assessing the quality of the considered solvers, 12 different problem instances have been generated based on real-world settings. Results obtained by each method after 10 independent runs have been compared using Friedman's non-parametric tests. Findings reached from the tests carried out lad to the creation of a multi-algorithm system, capable of applying different techniques according to the user's needs.Item PIACERE Project: Description and Prototype for Optimizing Infrastructure as Code Deployment Configurations(Association for Computing Machinery, Inc, 2022-07-09) Osaba, Eneko; Diaz-De-Arcaya, Josu; Orue-Echevarria, Leire; Alonso, Juncal; Lobo, Jesus L.; Benguria, Gorka; Etxaniz, Iñaki; Quantum; HPA; Tecnalia Research & Innovation; IAPIACERE is an European project supported by the Union's Horizon 2020 research and innovation programme, whose objective is to enhance the productivity of DevOps teams in the operation of Infrastructure as Code (IaC) by offering an integrated DevSec-Ops framework. Thus, DevOps practitioners can develop IaC as if they were programming a common software application. In order to achieve this challenging task, one of the core technologies considered within PIACERE will be the design and development of optimization metaheuristics, in a module coined as IaC Optimizer Platform (IOP). The main objective of the IOP is to provide DevSecOps teams with the most appropriate deployment configurations that best fit a set of defined constraints. The goal of this technical paper is to describe the preliminary approach followed in PIACERE for carrying out this optimization, and how the IOP fits into the whole PIACERE ecosystem. Additionally, results obtained in a preliminary experimentation are detailed in this study.