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Ant colony optimization inspired resource discovery in P2P Grid systems

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Abstract

It is a challenge for the traditional centralized or hierarchical Grid architecture to manage the large-scale and dynamic resources, while providing scalability. The Peer-to-Peer (P2P) model offers a prospect of dynamicity, scalability, and availability of a large pool of resources. By integrating the P2P philosophy and techniques into a Grid architecture, P2P Grid system is emerging as a promising platform for executing large-scale, resource intensive applications. There are two typical resource discovery approaches for a large-scale P2P system. The first one is an unstructured approach which propagates the query messages to all nodes to locate the required resources. The method does not scale well because each individual query generates a large amount of traffic and the network quickly becomes overwhelmed by the messages. The second one is a structured approach which places resources at specified locations to make subsequent queries easier to satisfy. However, the method does not support multi-attribute range queries and may not work well in the network which has an extremely transient population. This paper proposes and designs a large-scale P2P Grid system which employs an Ant Colony Optimization (ACO) algorithm to locate the required resources. The ACO method avoids a large-scale flat flooding and supports multi-attribute range query. Multiple ants can be employed to improve the parallelism of the method. A simulator is developed to evaluate the proposed resource discovery mechanism. Comprehensive simulation results validate the effectiveness of the proposed method compared with the traditional unstructured and structured approaches.

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Authors and Affiliations

  1. EMC Research China, Beijing, 100084, People’s Republic of China

    Yuhui Deng

  2. Center for Grid Computing, Cambridge-Cranfield High Performance Computing Facilities, Cranfield University Campus, Bedfordshire, MK43 0AL, UK

    Frank Wang & Adrian Ciura

Authors
  1. Yuhui Deng
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  2. Frank Wang
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  3. Adrian Ciura
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Correspondence to Yuhui Deng.

Additional information

The initial work in this paper was conducted when Yuhui Deng was a research officer in Cranfield University. This paper was revised after Yuhui Deng joined EMC research China as a senior research scientist.

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Deng, Y., Wang, F. & Ciura, A. Ant colony optimization inspired resource discovery in P2P Grid systems. J Supercomput 49, 4–21 (2009). https://doi.org/10.1007/s11227-008-0214-0

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  • DOI: https://doi.org/10.1007/s11227-008-0214-0

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