An Adaptive Expanding AntBudget search algorithm for unstructured P2P networks

Traditional searching mechanisms in Gnutella-like unstructured P2P networks consume a large amount of network resources. In the previous work, we proposed an Expanding-Ring-like AntBudget search algorithm (ERAntBudget), which adopts Ant Colony Optimization principle and budget mechanism to improve search efficiency. However, this method ignores the potentiality of budget mechanism and results in unnecessary search traffic. In this paper, we propose an Adaptive Expanding AntBudget search algorithm (AEAntBudget), in which the search scope is progressively expanded according to the result of previous search step. AEAntBudget exploits budget mechanism to obtain finer-grain control of search scope in comparison with TTL-based search mechanisms. The results of simulation experiments show a significant performance improvement. The traffic cost is decreased by 36% without sacrificing searching quality.