Person: TOPCUOĞLU, HALUK RAHMİ
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TOPCUOĞLU
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HALUK RAHMİ
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Publication Metadata only Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing(ELSEVIER, 2020) TOPCUOĞLU, HALUK RAHMİ; Ismayilov, Goshgar; Topcuoglu, Haluk RahmiWorkflow scheduling is a largely studied research topic in cloud computing, which targets to utilize cloud resources for workflow tasks by considering the objectives specified in QoS. In this paper, we model dynamic workflow scheduling problem as a dynamic multi-objective optimization problem (DMOP) where the source of dynamism is based on both resource failures and the number of objectives which may change over time. Software faults and/or hardware faults may cause the first type of dynamism. On the other hand, confronting real-life scenarios in cloud computing may change number of objectives at runtime during the execution of a workflow. In this study, we propose a prediction based dynamic multi-objective evolutionary algorithm, called NN-DNSGA-II algorithm, by incorporating artificial neural network with the NSGA-II algorithm. Additionally, five leading non-prediction based dynamic algorithms from the literature are adapted for the dynamic workflow scheduling problem. Scheduling solutions are found by the consideration of six objectives: minimization of makespan, cost, energy and degree of imbalance; and maximization of reliability and utilization. The empirical study based on real-world applications from Pegasus workflow management system reveals that our NN-DNSGA-II algorithm significantly outperforms the other alternatives in most cases with respect to metrics used for DMOPs with unknown true Pareto-optimal front, including the number of non-dominated solutions, Schott's spacing and Hypervolume indicator. (C) 2019 Elsevier B.V. All rights reserved.Publication Metadata only A general framework based on dynamic multi-objective evolutionary algorithms for handling feature drifts on data streams(ELSEVIER, 2020) TOPCUOĞLU, HALUK RAHMİ; Sahmoud, Shaaban; Topcuoglu, Haluk RahmiThis paper proposes a new and efficient framework to deal with the classification of data streams when exhibiting feature drifts. The first building block of the framework is a dynamic multi-objective evolutionary algorithm called Dynamic Filter-Based Feature Selection (DFBFS) algorithm, which handles feature drifts by continuously selecting the optimal set during the stream processing. Moreover, a new feature drift detection method is proposed to incorporate with the DFBFS algorithm. In the proposed framework, the Artificial Neural Network (ANN) is utilized to classify the data streams by only focusing on the features selected by the DFBFS algorithm. The empirical study for evaluating the framework performance utilizes four different dataset generators by varying environmental parameters in terms of change severity and change frequency. Experimental evaluation validates our framework, as it significantly outperforms reference algorithms in terms of classification accuracy and the ability of fast recovery after the occurrence of feature drifts on the evaluated datasets. (C) 2019 Elsevier B.V. All rights reserved.Publication Metadata only Exploiting characterization of dynamism for enhancing dynamic multi-objective evolutionary algorithms(ELSEVIER, 2019) TOPCUOĞLU, HALUK RAHMİ; Sahmoud, Shaaban; Topcuoglu, Haluk RahmiCharacterization of dynamism is an essential phase for some of the dynamic multi-objective evolutionary algorithms (DMOEAs) in order to improve their performance. Although frequency of change and severity of change are the two main perspectives of characterizing dynamic features of the dynamic multi-objective optimization problems (DMOPs), they do not sufficiently attract attentions of the research community. In this paper, we propose a set of new sensor-based change detection schemes for the DMOPs that significantly outperform the current used change detection schemes. Additionally, a new technique is proposed for detecting the change severity for DMOPs. The experimental evaluation based on different test problems and change severity levels validates performance of our technique. We also propose a novel adaptive algorithm called change-responsive NSGA-II (CR-NSGA-II) algorithm that incorporates the change detection schemes, the technique for change severity and a new response mechanism into the NSGA-II algorithm. Our algorithm demonstrates competitive and significantly better results than the leading DMOEAs on majority of test problems and metrics considered. (C) 2019 Elsevier B.V. All rights reserved.