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Understanding decentralised control of resource allocation in a minimal multi-agent system

Published: 14 May 2007 Publication History

Abstract

In response to the advent of new computational infrastructures, a number of initiatives, such as autonomic computing [5] and utility computing [8], have been announced by major IT vendors sharing the same underlying principles of provisioning distributed computational resources to a large number of users "on demand". Since, by their nature, such systems are large, open and dynamic, allocation of resources to users presents unique challenges that threaten to overwhelm existing centralised management approaches [2].

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S. Bullock and D. Cliff. Complexity and emergent behaviour in ICT systems. Technical Report HP-2004-187, Hewlett-Packard Labs, 2004.
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T. Hogg and B. A. Huberman. Controlling chaos in distributed systems. IEEE Transactions on Systems, Man and Cybernetics, 21:1325--1332, 1991.
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J. O. Kephart and D. M. Chess. The vision of autonomic computing. IEE Computer, 36(1):41--50, 2003.
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H. V. D. Parunak and S. A. Brueckner. Engineering swarming systems. In F. Bergenti, M.-P. Gleizes, and F. Zambonelli, editors, Methodologies and Software Engineering for Agent Systems, pages 341--376. Kluwer, 2004.
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M. A. Rappa. The utility business model and the future of computing services. IBM Systems Journal, 43:32--42, 2003.
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  • (2013)Distributed Resource Search in Self-organising NetworksProceedings of the 6th International Conference on Industrial Applications of Holonic and Multi-Agent Systems - Volume 806210.1007/978-3-642-40090-2_24(269-280)Online publication date: 26-Aug-2013
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  1. Understanding decentralised control of resource allocation in a minimal multi-agent system

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    cover image ACM Other conferences
    AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
    May 2007
    1585 pages
    ISBN:9788190426275
    DOI:10.1145/1329125
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 14 May 2007

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    View all
    • (2024)Battlefield Transfers in Coalitional Blotto GamesProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663032(1710-1717)Online publication date: 6-May-2024
    • (2021)Proactive auto-scaling for cloud environments using temporal convolutional neural networksJournal of Parallel and Distributed Computing10.1016/j.jpdc.2021.04.006154(119-141)Online publication date: Aug-2021
    • (2013)Distributed Resource Search in Self-organising NetworksProceedings of the 6th International Conference on Industrial Applications of Holonic and Multi-Agent Systems - Volume 806210.1007/978-3-642-40090-2_24(269-280)Online publication date: 26-Aug-2013
    • (2012)Applying reinforcement learning towards automating resource allocation and application scalability in the cloudConcurrency and Computation: Practice and Experience10.1002/cpe.286425:12(1656-1674)Online publication date: 30-May-2012
    • (2010)Adaptive negotiation in managing wireless sensor networksProceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems10.1007/978-3-642-25920-3_9(121-136)Online publication date: 12-Nov-2010
    • (2010)An Autonomy-Oriented Computing Mechanism for Modeling the Formation of Energy Distribution Networks: Crude Oil Distribution in U.S. and CanadaLife System Modeling and Intelligent Computing10.1007/978-3-642-15597-0_45(410-420)Online publication date: 2010

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