An Evolutionary Approach to Task Graph Scheduling

Parsa, Saeed; Lotfi, Shahriar; Lotfi, N.

doi:10.1007/978-3-540-71618-1_13

Cited by 9 publications

(23 citation statements)

References 12 publications

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“…Fig. 1 shows an example of a tasks graph [1]. The target is finding an optimal schedule for tasks graph execution on a multi-processor architecture where the total time of execution as the time of the last work unit to be minimized.…”

Section: Scheduling Tasks Graphmentioning

confidence: 99%

“…This queue is arranged based on the earliest possible start time as non-down for arranged work units. The algorithm calculates the earliest possible start time for each node (n i ) is as follows in which the critical path and the length of them is easily to be calculated [1]. Table II shows a sample chromosome structure.…”

Section: B How To Generate Initial Populationmentioning

confidence: 99%

“…If all members of the initial population will be generating with this algorithm, then it is will be faced to the risk of premature convergence and replicating solutions. Therefore, it is better to adding some number of members randomly to the population in order to maintain the diversity of population [1].…”

Section: B How To Generate Initial Populationmentioning

confidence: 99%

“…Another type of it, a work unit is randomly selected and randomly will be changed the related processor of it. On the other type of mutation, which used in this article, it is selected a work unit randomly and we will change the related processor of it randomly [1]. Table IV shows a sample mutation operation.…”

Section: E Mutation Operatormentioning

confidence: 99%

“…The timing should be done in such a way that the total execution time of a program with regarding to minimize time of tasks and communication between processors. The task graph scheduling problem is a NP Hard problem [1], [2]. Therefore approaches based on deterministic methods will not work in this field.…”

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithm with Descendants Idea for Scheduling Tasks Graph in the Multi-Processor Architecture

Mahi¹,

Kodaz²

2014

JACN

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Abstract-Nowadays, multi-processor systems are being used extensively in parallel computing. The effective scheduling system for implementing parallel programs to achieve high performance is crucial. The timing should be done in such a way that the total execution time of the program with according to time and tasks to be minimize communication between processors. This is a NP-Hard problem which tasks graph scheduling approaches based on deterministic methods are not effective in this context while the use of evolutionary computing and genetic algorithms to solve this problem effectively are mainly. In this paper, a new genetic algorithm is proposed for the problem of scheduling tasks graph which is able to spend less time to get. This algorithm is based on a new approach to minimize the length of the critical path and the cost of communication between processors. In this paper, we calculate the number of descendants for each node in which tries to minimize the total execution time of the program.

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Section: Scheduling Tasks Graphmentioning

confidence: 99%

Section: B How To Generate Initial Populationmentioning

confidence: 99%

Section: B How To Generate Initial Populationmentioning

confidence: 99%

Section: E Mutation Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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