Person: TOPCUOĞLU, HALUK RAHMİ
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TOPCUOĞLU
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HALUK RAHMİ
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Publication Open Access Quantifying the impact of data replication on error propagation(2022-09-01) ÖZTÜRK, ZUHAL; TOPCUOĞLU, HALUK RAHMİ; ÖZTÜRK Z., TOPCUOĞLU H. R. , Kandemir M. T.Various technological developments in the microprocessor world make modern computing systems more vulnerable to soft errors than in the past, and consequently fault tolerance techniques are becoming increasingly important in various application domains. While in general fault tolerance methods are known to achieve high levels of reliability, they can also introduce significant performance, energy, and memory overheads, which can be reduced by employing such techniques selectively, as opposed to indiscriminately. Data Replication is used to prevent error propagation across hardware components and application program data structures by replicating application program\"s data. When using data replication, many factors need to be taken into account, including which data structures/elements to replicate, how many times to replicate a given data element, and which threads to protect (in a multithreaded application). These and similar factors define what can be termed as \"replication space\". This study defines a replication space, and systematically explores protection techniques of various strengths/degrees, quantifying their impacts on memory consumption, performance, and error propagation. Our experimental analysis reveals that different degrees of protection levels bring different outcomes based on the application specifics. In particular, while error propagation is limited, to a certain extent, when employing data replication in multithreaded applications where the thread do not communicate/share data much, the speed of error propagation across threads can be quite fast in applications where threads are more tightly coupled. Additionally, our results indicate that in certain cases where error propagation is low, the effect of data replication on error propagation can be negligible.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 Open Access Network Congestion Aware Multiobjective Task Scheduling in Heterogeneous Fog Environments(2023-01-01) TOPCUOĞLU, HALUK RAHMİ; Altin L., TOPCUOĞLU H. R., Gurgen F. S.Task scheduling on fog environments surges new challenges compared to scheduling on conventional cloud computing. Various levels of heterogeneity and dynamism cause task scheduling problem is more challenging for fog computing. In this study, we present a multiobjective task scheduling model with a total of five objectives and propose a multiobjective multirank (MOMRank) scheduling algorithm for fog computing. The performance of the proposed strategy is assessed with well-known multiobjective metaheuristics [the nondominated sorting genetic algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2)] and a widely used algorithm from the literature, the multiobjective heterogeneous earliest finish time (MOHEFT) algorithm using three common multiobjective metrics. Additionally, we incorporate two task clustering mechanisms to the algorithms in order to improve data transmissions on interconnection networks. Results of empirical evaluations given in performance profiles over all problem instances validate significance of both our algorithm and the integrated extensions for diminishing data transfer costs.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 Open Access Studying error propagation on application data structure and hardware(2022-11-01) ÖZTÜRK, ZUHAL; TOPCUOĞLU, HALUK RAHMİ; ÖZTÜRK Z., TOPCUOĞLU H. R., Kandemir M. T.As technology scales, transistors become smaller and aggressive power optimization techniques combined with high operation frequencies and performance-enhancing microarchitectural techniques are employed to achieve increasingly higher performance and power efficiencies. Unfortunately, these developments make the modern systems more vulnerable to soft errors, which are becoming a critical issues in both hardware and software domains. Motivated by this observation, in this work, we propose, implement, and evaluate two error propagation metrics in order to characterize error propagation at both software and hardware levels. The first metric aims to measure error propagation on program data structures, whereas the second one measures the fraction of corrupted locations in the cache memory structure for a given period of time. We evaluate our proposed metrics by performing an empirical study of two application programs using both single-threaded and multi-threaded executions, and varying various experimental parameters such as thread count, error rate, location of errors, and architectural parameters. Our extensive experimental analysis reveals that error propagation over program data structures is highly dependent on application behavior.Further, depending on the cache parameters used, propagation of errors on cache can exhibit different patterns. This paper also discusses how our observed error propagation trends in program data structures and data caches are correlated with each other, focusing in particular on the differences in error propagation speeds in application data structures and data caches.Publication Open Access Dynamic multi-objective evolutionary algorithms in noisy environments(2023-07-01) TOPCUOĞLU, HALUK RAHMİ; Sahmoud S., TOPCUOĞLU H. R.Real-world multi-objective optimization problems encounter different types of uncertainty that may affect the quality of solutions. One common type is the stochastic noise that contaminates the objective functions. Another type of uncertainty is the different forms of dynamism including changes in the objective functions. Although related work in the literature targets only a single type, in this paper, we study Dynamic Multi-objective Optimization problems (DMOPs) contaminated with stochastic noises by dealing with the two types of uncertainty simultaneously. In such problems, handling uncertainty becomes a critical issue since the evolutionary process should be able to distinguish between changes that come from noise and real environmental changes that resulted from different forms of dynamism. To study both noisy and dynamic environments, we propose a flexible mechanism to incorporate noise into the DMOPs. Two novel techniques called Multi-Sensor Detection Mechanism (MSD) and Welford-Based Detection Mechanism (WBD) are proposed to differentiate between real change points and noise points. The proposed techniques are incorporated into a set of Dynamic Multi-objective Evolutionary Algorithms (DMOEAs) to analyze their impact. Our empirical study reveals the effectiveness of the proposed techniques for isolating noise from real dynamic changes and diminishing the noise effect on performance.Publication Metadata only A new prediction-based algorithm for dynamic multi-objective optimization problems(2023-01-01) TOPCUOĞLU, HALUK RAHMİ; Karkazan K., TOPCUOĞLU H. R., Sahmoud S.The mechanism for reacting to the changes in an environment when detected is the key issue that distinguishes various algorithms proposed for dynamic multi-objective optimization problems (DMOPs). The severity of change is a significant approach to identify the dynamic characteristics of DMOPs. In this paper, a prediction-based strategy based on utilizing the degree of the changes is presented to address environmental changes. In case of a change detection in the given DMOP, the severity of change is evaluated and an appropriate reaction mechanism is followed based on the degree of the observed change. To accelerate the convergence process, the algorithm may respond multiple times for the same change. The performance of our algorithm is evaluated by comparing it with dynamic multi-objective evolutionary algorithms using six benchmarks. The effectiveness of our algorithm is demonstrated in the experimental study where it outperforms other compared algorithms in most of the tested instances considered.