An incremental genetic algorithm approach to multiprocessor scheduling

Wu, Annie S.; Han, Yu; Jin, Shidai; Lin, Kuo-Chi; Schiavone, Guy A.

doi:10.1109/tpds.2004.38

Cited by 209 publications

(173 citation statements)

References 28 publications

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“…Using a time stopping criterion of 90 s, TS improved over the previous best-known results in ten HCSP instances by Braun et al Many related variants of the HCSP have been faced using EAs. The GA by Wu et al (2004) followed an incremental approach that gradually increases the difficulty of fitness values until finding an adequate schedule for precedence-constrained tasks in a multiprocessor system. However, the experimental analysis only considered four processors, and while the GA outperformed traditional scheduling methods in terms of solution quality, it needed large a population size, so the scalability of the approach to large problems is compromised.…”

Section: Related Work: Eas For Hc Schedulingmentioning

confidence: 99%

Heterogeneous computing scheduling with evolutionary algorithms

2010

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This work presents sequential and parallel evolutionary algorithms (EAs) applied to the scheduling problem in heterogeneous computing environments, a NP-hard problem with capital relevance in distributed computing. These methods have been specifically designed to provide accurate and efficient solutions by using simple operators that allow them to be later extended for solving realistic problem instances arising in distributed heterogeneous computing (HC) and grid systems. The EAs were codified over MALLBA, a general-purpose library for combinatorial optimization. Efficient numerical results are reported in the experimental analysis performed on wellknown problem instances. The comparative study of scheduling methods shows that the parallel versions of the implemented evolutionary algorithms are able to achieve high problem solving efficacy, outperforming traditional scheduling heuristics and also improving over previous results already reported in the related literature.

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Section: Related Work: Eas For Hc Schedulingmentioning

confidence: 99%

Heterogeneous computing scheduling with evolutionary algorithms

2010

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“…We present the comparative evaluation of HG algorithm and the existing algorithms for heterogeneous system such as HEFT and GA algorithm [12] for DAGs with various characteristics by simulation. For our experiments, a variety of synthetic DAGs and heterogeneous systems were generated using a random graph generator and a random heterogeneous system generator.…”

Section: Performance Analyses and Discussionmentioning

confidence: 99%

“…For this reason, there has been considerable research into heuristic static task scheduling algorithms [2]. These heuristics are classified into a variety of categories such as list scheduling algorithms [3][4][5], clustering algorithms [6], task duplication based algorithms [7][8] and Genetic algorithms [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Scheduling Algorithm to Heterogeneous Distributed System

Kang¹,

Zhang²

2012

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In parallel and distributed computing, development of an efficient static task scheduling algorithm for directed acyclic graph (DAG) applications is an important problem. The static task scheduling problem is NP-complete in its general form. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, consisting of processors with varying processing capabilities and network links with varying bandwidths. List scheduling algorithms are generally preferred since they generate good quality schedules with less complexity. But these list algorithms leave a lot of room for improvement, especially when these algorithms are used in specialized heterogeneous environments This paper presents an hybrid genetic task scheduling algorithm for the tasks run on the network of heterogeneous systems and represented by Directed Acyclic Graphs (DAGs). First, the algorithm assigns a coupling factor to each task to present the tasks should be scheduled onto the same processor by avoiding the large communication time. Second, the algorithm generate some high quality initial solution by scheduling the tasks which are strongly coupled with each other onto the same processor, and improve the quality of the solution by using coupling initial solutions, random solution, near optimal solutions obtained by the list scheduling algorithm in the crossover and mutation operator. The performance of the algorithm is illustrated by comparing with the existing effectively scheduling algorithms.

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“…A Genetic algorithm is a valuable population based approach for the multiprocessor task scheduling. Consequently many researchers [6,12,19] have reported success withgenetic algorithm in achieving good solutions to combinatorial optimization problems. A genetic algorithm starts with an initial population which can be generated arbitrarily or based on some rules, heuristics and algorithms.…”

Section: Proposed Hybrid Genetic Algorithm (Hga)mentioning

confidence: 99%

Hybrid Genetic Algorithm for Bi-Criteria Multiprocessor Task Scheduling with Communication Delay

Dhingra¹,

Dhingra²,

Gupta³

et al. 2016

ACII

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Present work considers the minimization of the bi-criteria function including weighted sum of makespan and total completion time for a Multiprocessor task scheduling problem.Genetic algorithm is the most Keywords:Multiprocessor task scheduling, heuristics, Hybrid Genetic algorithm, makespan, Total Completion time. 1.INTRODUCTIONParallel processing is the most promising approach for meeting the increased computational requirements thathas introduced a number of problems including multiprocessor task scheduling. Usually a large program is divided into smaller tasks havingsome dependencies representing the precedence constraints such that a task cannot be started until all its predecessors have finished. The goal of a task scheduling algorithm is to schedule all the tasks on the given number of available processors without violating precedence constraints so as to minimize the different performance measures in order to maximize the throughput and utilization of the system. The multiprocessor task scheduling problem with precedence constraints belongs to the class of NP hard problems and its importance led to several comparative studies. The conventional research on scheduling considers heuristic based algorithms which are divided into three categories: list scheduling, clustering based and duplication based. Among heuristic algorithms, List scheduling algorithms are commonly used and work by assigning priority to each task according to attributes such as t-level (top level), b-level (bottom level), static level (sl) and ALAP (As Late As Possible)

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An incremental genetic algorithm approach to multiprocessor scheduling

Cited by 209 publications

References 28 publications

Heterogeneous computing scheduling with evolutionary algorithms

Heterogeneous computing scheduling with evolutionary algorithms

A Hybrid Genetic Scheduling Algorithm to Heterogeneous Distributed System

Hybrid Genetic Algorithm for Bi-Criteria Multiprocessor Task Scheduling with Communication Delay

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