A Novel Resource Optimization Algorithm Based on Clustering and Improved Differential Evolution Strategy under a Cloud Environment

Zhou, Z; Li, F; Yang, S

A Novel Resource Optimization Algorithm Based on Clustering and Improved Differential Evolution Strategy under a Cloud Environment

Zhou, Z Li, F Yang, S

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Asian and Low-Resource Language Information Processing, 2021, 20, (5), pp. 1-15
Issue Date:: 2021-09-01

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Field	Value	Language
dc.contributor.author	Zhou, Z
dc.contributor.author	Li, F
dc.contributor.author	Yang, S https://orcid.org/0000-0002-6772-6805
dc.date.accessioned	2022-05-15T21:40:08Z
dc.date.available	2022-05-15T21:40:08Z
dc.date.issued	2021-09-01
dc.identifier.citation	ACM Transactions on Asian and Low-Resource Language Information Processing, 2021, 20, (5), pp. 1-15
dc.identifier.issn	2375-4699
dc.identifier.issn	2375-4702
dc.identifier.uri	http://hdl.handle.net/10453/157374
dc.description.abstract	Resource optimization algorithm based on clustering and improved differential evolution strategy, as a new global optimized algorithm, has wide applications in language translation, language processing, document understanding, cloud computing, and edge computing due to high efficiency. With the development of deep learning technology and the rise of big data, the resource optimization algorithm encounters a series of challenges, such as the workload imbalance and low resource utilization. To address the preceding problems, this study proposes a novel resource optimization algorithm based on clustering and an improved differential evolution strategy (Multi-objective Task Scheduling Strategy (MTSS)). Three indexes, namely task completion time, execution cost, and workload, of virtual machines are selected and used to build the fitness function of the MTSS algorithm. At the same time, the preprocessing state is set up to cluster according to the resource and task characteristics to reduce the magnitude of their matching scale. Moreover, to solve the workload imbalance among different resource sets, local resource tasks are reallocated using the Q-value method in the MTSS strategy to achieve workload balance of global resources and improve the resource utilization rate. Experiments are carried out to evaluate the effectiveness of the proposed algorithm. Results show that the proposed algorithm outperforms other algorithms in terms of task completion time, execution cost, and workload balancing.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Asian and Low-Resource Language Information Processing
dc.relation.isbasedon	10.1145/3462761
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Novel Resource Optimization Algorithm Based on Clustering and Improved Differential Evolution Strategy under a Cloud Environment
dc.type	Journal Article
utslib.citation.volume	20
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-15T21:40:06Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	5

Abstract:

Resource optimization algorithm based on clustering and improved differential evolution strategy, as a new global optimized algorithm, has wide applications in language translation, language processing, document understanding, cloud computing, and edge computing due to high efficiency. With the development of deep learning technology and the rise of big data, the resource optimization algorithm encounters a series of challenges, such as the workload imbalance and low resource utilization. To address the preceding problems, this study proposes a novel resource optimization algorithm based on clustering and an improved differential evolution strategy (Multi-objective Task Scheduling Strategy (MTSS)). Three indexes, namely task completion time, execution cost, and workload, of virtual machines are selected and used to build the fitness function of the MTSS algorithm. At the same time, the preprocessing state is set up to cluster according to the resource and task characteristics to reduce the magnitude of their matching scale. Moreover, to solve the workload imbalance among different resource sets, local resource tasks are reallocated using the Q-value method in the MTSS strategy to achieve workload balance of global resources and improve the resource utilization rate. Experiments are carried out to evaluate the effectiveness of the proposed algorithm. Results show that the proposed algorithm outperforms other algorithms in terms of task completion time, execution cost, and workload balancing.

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http://hdl.handle.net/10453/157374