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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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 36; no. 1; pp. 1 - 14
Main Authors: Jiang, Junqiang, Sun, Zhifang, Lu, Ruiqi, Pan, Li, Peng, Zebo
Format: Journal Article
Language:English
Published: IEEE 01.01.2025
Subjects:
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
Online Access:Get full text
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Summary: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.
ISSN:1045-9219
1558-2183
1558-2183
DOI:10.1109/TPDS.2024.3492210