Browsing by Author "Sekaran K, C."

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    Q-feedan effective solution for the free-riding problem in unstructured P2P networks
    (2010) Thampi, S.M.; Sekaran K, C.
    This paper presents a solution for reducing the ill effects of free-riders in decentralised unstructured P2P networks. An autonomous replication scheme is proposed to improve the availability and enhance system performance. Q-learning is widely employed in different situations to improve the accuracy in decision making by each peer. Based on the performance of neighbours of a peer, every neighbour is awarded different levels of ranks. At the same time a low-performing node is allowed to improve its rank in different ways. Simulation results show that Q-learning-based free riding control mechanism effectively limits the services received by free-riders and also encourages the low-performing neighbours to improve their position. The popular files are autonomously replicated to nodes possessing required parameters. Due to this improvement of quantity of popular files, free riders are given opportunity to lift their position for active participation in the network for sharing files. Q-feed effectively manages queries from free riders and reduces network traffic significantly. © 2010 S. M. Thampi and C. Sekaran K.
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    Q-learning based collaborative load balancing using distributed search for unstructured P2P networks
    (2008) Thampi, S.M.; Sekaran K, C.
    Peer-to-peer structures are becoming more and more popular and an exhilarating new class of ground-breaking, internet-based data management systems. Query load balancing is an important problem for the efficient operation of unstructured P2P networks. The key issue is to identify overloaded peers and reassign their loads to others. This paper proposes a novel mobile agent based two-way load balancing technique for dynamic unstructured P2P networks. In this scheme, target peers are selected based on the result of reinforcement learning. Simulation results indicate that our technique manages the load on peers effectively and increases the search performance significantly. ©2008 IEEE.