Redesigning the jMetal Multi-Objective Optimization Framework

jMetal, an open source, Java-based framework for multi-objective optimization with metaheuristics, has become a valuable tool for many researches in the area as well as for some industrial partners in the last ten years. Our experience using and maintaining it during that time, as well as the received comments and suggestions, have helped us improve the jMetal design and identify significant features to incorporate. This paper revisits the jMetal architecture, describing its refined new design, which relies on design patterns, principles from object-oriented design, and a better use of the Java language features to improve the quality of the code, without disregarding jMetal ever goals of simplicity, facility of use, flexibility, extensibility and portability. Among the newly incorporated features, jMetal supports live interaction with running algorithms and parallel execution of algorithms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Predicting Workflow Task Execution Time in the Cloud using A Two-Stage Machine Learning Approach

Many techniques such as scheduling and resource provisioning rely on performance prediction of workflow tasks for varying input data. However, such estimates are difficult to generate in the cloud. This paper introduces a novel two-stage machine learning approach for predicting workflow task execution times for varying input data in the cloud. In order to achieve high accuracy predictions, our approach relies on parameters reflecting runtime information and two stages of predictions. Empirical results for four real world workflow applications and several commercial cloud providers demonstrate that our approach outperforms existing prediction methods. In our experiments, our approach respectively achieves a best-case and worst-case estimation error of 1.6% and 12.2%, while existing methods achieved errors beyond 20% (for some cases even over 50%) in more than 75% of the evaluated workflow tasks. In addition, we show that the models predicted by our approach for a specific cloud can be ported with low effort to new clouds with low errors by requiring only a small number of executions

A Study of the Combination of Variation Operators in the NSGA-II Algorithm

Multi-objective evolutionary algorithms rely on the use of variation operators as their basic mechanism to carry out the evolutionary process. These operators are usually fixed and applied in the same way during algorithm execution, e.g., the mutation probability in genetic algorithms. This paper analyses whether a more dynamic approach combining different operators with variable application rate along the search process allows to improve the static classical behavior. This way, we explore the combined use of three different operators (simulated binary crossover, differential evolution’s operator, and polynomial mutation) in the NSGA-II algorithm. We have considered two strategies for selecting the operators: random and adaptive. The resulting variants have been tested on a set of 19 complex problems, and our results indicate that both schemes significantly improve the performance of the original NSGA-II algorithm, achieving the random and adaptive variants the best overall results in the bi- and three-objective considered problems, respectively.UNIVERSIDAD DE MÁLAGA. CAMPUS DE EXCELENCIA INTERNACIONAL ANDALUCÍA TEC

Autotuning Stencil Computations with Structural Ordinal Regression Learning

Stencil computations expose a large and complex space of equivalent implementations. These computations often rely on autotuning techniques, based on iterative compilation or machine learning (ML), to achieve high performance. Iterative compilation autotuning is a challenging and time-consuming task that may be unaffordable in many scenarios. Meanwhile, traditional ML autotuning approaches exploiting classification algorithms (such as neural networks and support vector machines) face difficulties in capturing all features of large search spaces. This paper proposes a new way of automatically tuning stencil computations based on structural learning. By organizing the training data in a set of partially-sorted samples (i.e., rankings), the problem is formulated as a ranking prediction model, which translates to an ordinal regression problem. Our approach can be coupled with an iterative compilation method or used as a standalone autotuner. We demonstrate its potential by comparing it with state-of-the-art iterative compilation methods on a set of nine stencil codes and by analyzing the quality of the obtained ranking in terms of Kendall rank correlation coefficients

Optimización de problemas multiobjetivo de Ingeniería Civil con jMetal

Este artículo describe el uso del framework de optimización multiobjetivo jMetal para afrontar la resolución de problemas de ingeniería civil; en particular, lo que se ha hecho ha sido integrar un software Open Source para el diseño de estructuras, denominado Ebes, con jMetal. De esta forma los ingenieros civiles tienen a su disposición una herramienta que les permite diseñar estructuras que luego pueden ser optimizadas con metaheurísticas multiobjetivo atendiendo a varios criterios, como minimizar el peso y minimizar la deformación. Por otro lado, este tipo de problemas pueden ser objeto de estudios por parte de investigadores del área de las metaheurísticas, que pueden usarlos como casos de estudio. Tras presentar tanto jMetal como Ebes, se detalla la integración de ambas herramientas, se presentan tres casos de estudio y se proponen algunas líneas abiertas de investigación.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Bi-objective Workflow Scheduling in Production Clouds: Early Simulation Results and Outlook

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014.We present MOHEFT, a multi-objective list scheduling heuristic that provides the user with a set of Pareto tradeoff optimal solutions from which the one that better suits the user requirements can be manually selected. We demonstrate the potential of our method for multi-objective workflow scheduling on the commercial Amazon EC2 Cloud by comparing the quality of the MOHEFT tradeoff solutions with a state-of-the-art multi-objective approach called SPEA2* for three types of synthetic workflows with different parallelism and load balancing characteristics. We conclude with an outlook into future research towards closing the gap between the scientific simulation and real-world experimentation.The work presented in this paper has been partially supported by EU under the COST programme Action IC1305, Network for Sustainable Ultrascale Computing (NESUS)

Solving a Real-World Structural Optimization Problem With a Distributed SMS-EMOA Algorithm

This paper addresses a real-world optimization problem in civil engineering. It lies in the dimensioning of a 162m long bridge composed of 1584 bars so that both its weight and its deformation are to be minimized. Evaluating each possible configuration of the bridge takes several seconds and, as a consequence, running a metaheuristic for several thousands of evaluations would require many days on one single processor. Our approach has been to develop a distributed master/worker version of SMS-EMOA, an indicator-based multiobjective algorithm. By combining the Java implementation of the algorithm in jMetal with the Condor distributed scheduler, we have been able to use more than 350 cores to obtain accurate results in a reasonable amount of time.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Un Framework para Big Data Optimization Basado en jMetal y Spark

Las metaheurísticas multi-objetivo se han convertido en técnicas muy utilizadas para la resolución de problemas complejos de optimización compuestos de varias funciones objetivo en conflicto entre sí. Nos encontramos en la actualidad inmersos en la era del Big Data, por lo que los problemas multi-objetivo que surjan en este contexto cumplirán algunas de las cinco V’s que caracterizan a las aplicaciones Big Data (volumen, velocidad, variedad, veracidad, valor). Como consecuencia, las metaheurísticas deberán ser capaces de resolver problemas dinámicos, que pueden cambiar en el tiempo debido al procesamiento y análisis de diferentes fuentes de datos, que típicamente serán en streaming. En este trabajo presentamos el software jMetalSP, que combina el framework jMetal con Apache Spark. De esta forma, las metaheurísticas disponibles en jMetal se pueden adaptar fácilmente para resolver problemas dinámicos que se alimenten de distintas fuentes de datos en streaming, y que son gestionadas por Spark. Se describe la arquitectura de jMetalSP y se valida mediante un caso de uso realista basado en TSP bi-objetivo con datos abiertos reales de tráfico de la ciudad de Nueva York.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Study About Meta-Optimizing the NSGA-II Multi-Objective Evolutionary Algorithm.

The automatic design of multi-objective metaheuristics is an active research line aimed at, given a set of problems used as training set, to find the configuration of a multi-objective optimizer able of solving them efficiently. The expected outcome is that the auto-configured algorithm can be used of find accurate Pareto front approximations for other problems. In this paper, we conduct a study on the meta-optimization of the wellknown NSGA-II algorithm, i.e., we intend to use NSGA-II as an automatic configuration tool to find configurations of NSGA-II. This search can be formulated as a multi-objective problem where the decision variables are the NSGA-II components and parameters and the the objectives are quality indicators that have to be minimized. To develop this study, we rely on the jMetal framework. The analysis we propose is aimed at answering the following research questions: RQ1 - how complex is to build the meta-optimization package?, and RQ2 - can accurate configurations be found? We conduct an experimentation to give an answer to these questions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech