An Efficient Agent Control Method for Time-Constrained Applications

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  • TRUNG Tran Nguyen
    Department of Information Networking, Graduate School of Information Science and Technology, Osaka University
  • KAMADA Hideo
    Department of Information Networking, Graduate School of Information Science and Technology, Osaka University
  • KINOSHITA Kazuhiko
    Department of Information Networking, Graduate School of Information Science and Technology, Osaka University The Institute of Electronics, Information and Communication Engineers
  • YAMAI Nariyoshi
    Information Technology Center, Okayama University The Institute of Electronics, Information and Communication Engineers
  • TAKINE Tetsuya
    Devision of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University The Institute of Electronics, Information and Communication Engineers
  • MURAKAMI Koso
    Department of Information Networking, Graduate School of Information Science and Technology, Osaka University The Institute of Electronics, Information and Communication Engineers

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Abstract

As one of the technologies for the retrieval of desired contents over large-scale networks, multi-agent systems are receiving much attention. Since there are too many contents on the network to search them all exhaustively, some applications on multi-agent systems have time constraints, that is, they must obtain a result by a given deadline. To find better results for such applications, it is important for the agents to complete their tasks on as many nodes as possible by the deadline. However, most existing agent systems using round robin scheduling disciplines do not take time constraints into account. Therefore, agents are likely to miss their deadlines on many nodes. In this paper, we propose an efficient agent-dispatching method for time-constrained applications. This method decides creation and migration of a clone agent according to the estimated value of the number of agents that would have completed their tasks by the deadline. The results of our performance evaluation show that the proposed method increases the number of agents that complete their tasks.

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