Real Relative Encoding Genetic Algorithm for Workflow Scheduling in Heterogeneous Distributed Computing Systems

This paper introduces a novel Real Relative encoding Genetic Algorithm (R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:hr...

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Vydané v:	IEEE transactions on parallel and distributed systems Ročník 36; číslo 1; s. 1 - 14
Hlavní autori:	Jiang, Junqiang, Sun, Zhifang, Lu, Ruiqi, Pan, Li, Peng, Zebo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.01.2025
Predmet:	Biological cells Candidate task set directed acyclic graph (DAG) Distributed computing Encoding genetic algorithm Genetic algorithms Genetic operators Heuristic algorithms Metaheuristics Processor scheduling Quality of service real encoding Scheduling workflow scheduling workflow scheduling Heuristic algorithms Metaheuristics Candidate task set Quality of service Encoding Scheduling real encoding Distributed computing Biological cells Genetic algorithms Genetic operators genetic algorithm Processor scheduling directed acyclic graph (DAG)
ISSN:	1045-9219, 1558-2183, 1558-2183
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Abstract	This paper introduces a novel Real Relative encoding Genetic Algorithm (R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq1-3492210.gif"/> </inline-formula>GA) to tackle the workflow scheduling problem in heterogeneous distributed computing systems (HDCS). R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq2-3492210.gif"/> </inline-formula>GA employs a unique encoding mechanism, using real numbers to represent the relative positions of tasks in the schedulable task set. Decoding is performed by interpreting these real numbers in relation to the directed acyclic graph (DAG) of the workflow. This approach ensures that any sequence of randomly generated real numbers, produced by cross-over and mutation operations, can always be decoded into a valid solution, as the precedence constraints between tasks are explicitly defined by the DAG. The proposed encoding and decoding mechanism simplifies genetic operations and facilitates efficient exploration of the solution space. This inherent flexibility also allows R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq3-3492210.gif"/> </inline-formula>GA to be easily adapted to various optimization scenarios in workflow scheduling within HDCS. Additionally, R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq4-3492210.gif"/> </inline-formula>GA overcomes several issues associated with traditional genetic algorithms (GAs) and existing real-number encoding GAs, such as the generation of chromosomes that violate task precedence constraints and the strict limitations on gene value ranges. Experimental results show that R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq5-3492210.gif"/> </inline-formula>GA consistently delivers superior performance in terms of solution quality and efficiency compared to existing techniques.
AbstractList	This paper introduces a novel Real Relative encoding Genetic Algorithm (R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq1-3492210.gif"/> </inline-formula>GA) to tackle the workflow scheduling problem in heterogeneous distributed computing systems (HDCS). R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq2-3492210.gif"/> </inline-formula>GA employs a unique encoding mechanism, using real numbers to represent the relative positions of tasks in the schedulable task set. Decoding is performed by interpreting these real numbers in relation to the directed acyclic graph (DAG) of the workflow. This approach ensures that any sequence of randomly generated real numbers, produced by cross-over and mutation operations, can always be decoded into a valid solution, as the precedence constraints between tasks are explicitly defined by the DAG. The proposed encoding and decoding mechanism simplifies genetic operations and facilitates efficient exploration of the solution space. This inherent flexibility also allows R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq3-3492210.gif"/> </inline-formula>GA to be easily adapted to various optimization scenarios in workflow scheduling within HDCS. Additionally, R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq4-3492210.gif"/> </inline-formula>GA overcomes several issues associated with traditional genetic algorithms (GAs) and existing real-number encoding GAs, such as the generation of chromosomes that violate task precedence constraints and the strict limitations on gene value ranges. Experimental results show that R<inline-formula><tex-math notation="LaTeX">^{2}</tex-math> <mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="jiang-ieq5-3492210.gif"/> </inline-formula>GA consistently delivers superior performance in terms of solution quality and efficiency compared to existing techniques. This paper introduces a novel Real Relative encoding Genetic Algorithm (R(2)GA) to tackle the workflow scheduling problem in heterogeneous distributed computing systems (HDCS). R(2)GA employs a unique encoding mechanism, using real numbers to represent the relative positions of tasks in the schedulable task set. Decoding is performed by interpreting these real numbers in relation to the directed acyclic graph (DAG) of the workflow. This approach ensures that any sequence of randomly generated real numbers, produced by cross-over and mutation operations, can always be decoded into a valid solution, as the precedence constraints between tasks are explicitly defined by the DAG. The proposed encoding and decoding mechanism simplifies genetic operations and facilitates efficient exploration of the solution space. This inherent flexibility also allows R(2)GA to be easily adapted to various optimization scenarios in workflow scheduling within HDCS. Additionally, R(2)GA overcomes several issues associated with traditional genetic algorithms (GAs) and existing real-number encoding GAs, such as the generation of chromosomes that violate task precedence constraints and the strict limitations on gene value ranges. Experimental results show that R(2)GA consistently delivers superior performance in terms of solution quality and efficiency compared to existing techniques.
Author	Jiang, Junqiang Peng, Zebo Sun, Zhifang Lu, Ruiqi Pan, Li
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Keywords	workflow scheduling Heuristic algorithms Metaheuristics Candidate task set Quality of service Encoding Scheduling real encoding Distributed computing Biological cells Genetic algorithms Genetic operators genetic algorithm Processor scheduling directed acyclic graph (DAG)
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SubjectTerms	Biological cells Candidate task set directed acyclic graph (DAG) Distributed computing Encoding genetic algorithm Genetic algorithms Genetic operators Heuristic algorithms Metaheuristics Processor scheduling Quality of service real encoding Scheduling workflow scheduling
Title	Real Relative Encoding Genetic Algorithm for Workflow Scheduling in Heterogeneous Distributed Computing Systems
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