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An Agent-based Adaptive Mechanism for Efficient Job Scheduling in Open and Large-scale Environments

Journal Article


Abstract


  • Agent-based scheduling refers to applying intelligent agents to autonomously allocate resources to jobs. Decentralized agent-based scheduling approaches have achieved good performance in open and dynamic environments because the relationships of agents are flexible. For new jobs and resources and unexpected events, decentralized agents can respond adaptively and flexibly. Besides, decentralized approaches are easy to be extended because there is no central control agent that limits the scalability. However, decentralized approaches might have low efficiency in large-scale environments because behaviors of agents may be self-interested and competitive, due to their local views during decision making. When interacting with a large number of agents, each agent may spend a considerable amount of time on failed attempts before reaching the final agreements with other agents. To improve the efficiency of decentralized agent-based scheduling approaches in large-scale environments, and to keep the flexibility and adaptability of decentralized agents for the decision-making on scheduling, this paper provides a new agent-based adaptive mechanism for efficient job scheduling. A new type of agent named host agent is introduced to coordinate self-interested behaviors of agents without participating in the decision making of agents during job scheduling. The proposed mechanism was developed in JADE and tested in open and large-scale environments. The experimental results indicate that the proposed mechanism is effective and efficient in open and large-scale environments.

Publication Date


  • 2021

Citation


  • Yang, Y., Ren, F., & Zhang, M. (2021). An Agent-based Adaptive Mechanism for Efficient Job Scheduling in Open and Large-scale Environments. Journal of Systems Science and Systems Engineering, 30(4), 400-416. doi:10.1007/s11518-021-5494-4

Scopus Eid


  • 2-s2.0-85105943872

Web Of Science Accession Number


Start Page


  • 400

End Page


  • 416

Volume


  • 30

Issue


  • 4

Abstract


  • Agent-based scheduling refers to applying intelligent agents to autonomously allocate resources to jobs. Decentralized agent-based scheduling approaches have achieved good performance in open and dynamic environments because the relationships of agents are flexible. For new jobs and resources and unexpected events, decentralized agents can respond adaptively and flexibly. Besides, decentralized approaches are easy to be extended because there is no central control agent that limits the scalability. However, decentralized approaches might have low efficiency in large-scale environments because behaviors of agents may be self-interested and competitive, due to their local views during decision making. When interacting with a large number of agents, each agent may spend a considerable amount of time on failed attempts before reaching the final agreements with other agents. To improve the efficiency of decentralized agent-based scheduling approaches in large-scale environments, and to keep the flexibility and adaptability of decentralized agents for the decision-making on scheduling, this paper provides a new agent-based adaptive mechanism for efficient job scheduling. A new type of agent named host agent is introduced to coordinate self-interested behaviors of agents without participating in the decision making of agents during job scheduling. The proposed mechanism was developed in JADE and tested in open and large-scale environments. The experimental results indicate that the proposed mechanism is effective and efficient in open and large-scale environments.

Publication Date


  • 2021

Citation


  • Yang, Y., Ren, F., & Zhang, M. (2021). An Agent-based Adaptive Mechanism for Efficient Job Scheduling in Open and Large-scale Environments. Journal of Systems Science and Systems Engineering, 30(4), 400-416. doi:10.1007/s11518-021-5494-4

Scopus Eid


  • 2-s2.0-85105943872

Web Of Science Accession Number


Start Page


  • 400

End Page


  • 416

Volume


  • 30

Issue


  • 4