Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems

Chitra, P.; Rajaram, R.; Venkatesh, P.

doi:10.1016/j.asoc.2010.11.003

Cited by 49 publications

(44 citation statements)

References 17 publications

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“…A node with no parent is called entry node .A node with no child is called exit node [10]. A sample DAG is shown in Figure.1, which contains T 1 , T 2 , T 3 ,T 4 ,T 5 ,T 6 ,T 7 ,T 8 ,T 9, and T 10 tasks.T 1 is an entry node, which has no parent node and T 9 is an exit node, with no child node.T 2 is a parent node of T 4 and T 5 .…”

Section: 2schedulingmentioning

confidence: 99%

Dynamic Task Scheduling Methods in Heterogeneous Systems: A Survey

Amalarethinam¹,

Josphin²

2015

IJCA

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Heterogeneous systems create unlimited opportunities and challenges in the fields of parallel processing, design of algorithms, and partitioning and mapping of parallel tasks. In mapping of parallel tasks, scheduling plays an important role. The scheduling decisions are based on the dynamic parameters that may change during run time. The goal of scheduling is to utilize all the processors with minimum execution time by proper allocation of tasks to the processors. Task scheduling achieves high performance in the heterogeneous systems. A Parallel application can be represented by a Directed Acyclic Graph (DAG), which represents the dependency among tasks, based on their execution time and communication time. Previous studies reveal that many researchers have designed efficient scheduling algorithms with different parameters on multiprocessor systems. This paper investigates different aspects in scheduling and issues in various levels of the heterogeneous systems.

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Section: 2schedulingmentioning

confidence: 99%

Dynamic Task Scheduling Methods in Heterogeneous Systems: A Survey

Amalarethinam¹,

Josphin²

2015

IJCA

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“…In multiobjective memetic algorithms, the most popular scalarizing function is the weighted-sum aggregation [39,41], where a weighting coefficient vector represents the relative importance of each objective function. However, this approach cannot identify a number of Pareto optimal solutions, whose corresponding non-dominated objective vectors are located on the convex hull of the Pareto front [24,40].…”

Section: Achievement Scalarizing Functionmentioning

confidence: 99%

A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming

Liefooghe

Vérel

Hao

2014

Applied Soft Computing

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The conventional Unconstrained Binary Quadratic Programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. This paper extends the single-objective UBQP to the multiobjective case (mUBQP) where multiple objectives are to be optimized simultaneously. We propose a hybrid metaheuristic which combines an elitist evolutionary multiobjective optimization algorithm and a state-of-the-art single-objective tabu search procedure by using an achievement scalarizing function. Finally, we define a formal model to generate mUBQP instances and validate the performance of the proposed approach in obtaining competitive results on large-size mUBQP instances with two and three objectives.

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“…Various type of DAGs have been introduced which indicate macro data-flow of parallel programs written in a SPMD style for distributed-memory systems to serve task scheduling problems, such as Fast Fourier Transformation (FFT) [23], Guess-Jordan (G-J) [24], Guesselimination (G) [25], LU-decomposition [26] and Laplace equation solver [27]. Random Task Graph that produces random data flow graphs is also used in test beds [28]. In this paper, we assume that each task of DAG needs different processing time.…”

Section: Applicationmentioning

confidence: 99%

Untitled

2011

IJDPS

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Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems

Cited by 49 publications

References 17 publications

Dynamic Task Scheduling Methods in Heterogeneous Systems: A Survey

Dynamic Task Scheduling Methods in Heterogeneous Systems: A Survey

A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming

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