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Article | Open Access

A Hybrid Mashup Platform Based on Structured and Unstructured Peer-to-Peer Networks Empowered with Genetic Algorithms

Osama Al-Haj Hassan1( )Ammar Odeh1Abdullah Aref 2Ghassan Samara3
Department of Computer Science, Princess Sumaya University for Technology, Amman, Jordan
Department of Data Science, Princess Sumaya University for Technology, Amman, Jordan
Department of Computer Science, Zarqa University, Zarqa, Jordan
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Abstract

Mashups are among the key web technologies that provide end-users with customizable and personalized tools. Most mashup platforms are based on centralized architectures or do not employ fully decentralized architectures; therefore, in this paper, we propose a decentralized architecture for mashups that combines the strengths of structured and unstructured peer-to-peer networks. For the structured part, we rely on the Chord lookup protocol, and for the unstructured part, we build groups of nodes via two flavors of network flooding, namely, sequence number flooding and reverse path flooding. Brokers in the unstructured part would be responsible for hosting and executing mashups, such that deciding which brokers should host a given mashup is determined by utilizing genetic algorithms. We compare our work against several approaches that rely on random and greedy mashup placement. We also assess our proposed approach to pure structured and pure unstructured approaches. We evaluate our system using simulations, and results show that executing mashups using the version of our scheme that relies on reverse path flooding generates at least 25% lower delays than the other approaches.

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Computers, Materials & Continua
Article number: 56

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Cite this article:
Hassan OA-H, Odeh A, Aref A, et al. A Hybrid Mashup Platform Based on Structured and Unstructured Peer-to-Peer Networks Empowered with Genetic Algorithms. Computers, Materials & Continua, 2026, 88(3): 56. https://doi.org/10.32604/cmc.2026.083861

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Received: 13 April 2026
Accepted: 28 May 2026
Published: 23 July 2026
© The Author 2026.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.