A Hybrid P2P Enabled Big Data Resource Discovery
Method in Cloud Environments

The search for big data resources is the core foundational function of big data services in cloud environments. Currently, the search methods include centralized Service Oriented Architecture (SOA), structured Peer-to-Peer (P2P), and unstructured P2P. However, SOA has a single point of failure, structured P2P has a complex maintenance mechanism, and unstructured P2P has network congestion and slow search problems. Therefore, firstly, we adopt a hybrid P2P network as the topology structure of data resource nodes in the cloud environment, encapsulating data resources with services, and simplifying user access to data resources by matching service description information. Secondly, in order to further improve search efficiency and stability, an active replication protocol based on data index between supernodes is proposed. Finally, a flooding-based data resource search method among supernodes is proposed, which achieves scalable resource management and search, ensuring that the system can maintain efficient operation even when scaling up. The combination of these three provides a flexible infrastructure, high search efficiency, and scalability. Experiments have shown that under specific conditions, our proposed method reduces the number of messages to one percent of the Flooding network and reduces the average hop count by about 50% compared to the traditional hybrid P2P network.