Three Layer Hierarchical Model for Chord

TL;DR

This paper proposes a three-layer hierarchical P2P model using an optimized Chord algorithm to improve search speed, reduce lookup latency, and enhance fault tolerance in decentralized networks.

Contribution

It introduces a novel three-layer hierarchy with distributed databases and an optimized Chord algorithm to improve P2P network efficiency and scalability.

Findings

Reduced lookup latency by decreasing the number of hops.

Faster search performance in the hierarchical model.

Improved fault tolerance and network management.

Abstract

Increasing popularity of decentralized P2P architecture emphasizes on the need to come across an overlay structure that can provide efficient content discovery mechanism, accommodate high churn rate and adapt to failures. Traditional p2p systems are not able to solve the problems relating scalability and high churn rates. Hierarchical model were introduced to provide better fault isolation, effective bandwidth utilization, a superior adaptation to the underlying physical network and a reduction of the lookup path length as additional advantages. It is more efficient and easier to manage than traditional p2p networks. This paper discusses a further step in p2p hierarchy via 3-layers hierarchical model with distributed database architecture in different layer, each of which is connected through its root. The peers are divided into three categories according to their physical stability and strength. They are Ultra Super-peer, Super-peer and Ordinary Peer and we assign these peers to first, second and third level of hierarchy respectively. Peers in a group in lower layer have their own local database which hold as associated super-peer in middle layer and access the database among the peers through user queries. In our 3-layer hierarchical model for DHT algorithms, we used an advanced Chord algorithm with optimized finger table which can remove the redundant entry in the finger table in upper layer that influences the system to reduce the lookup latency. Our research work finally resulted that our model really provides faster search since the network lookup latency is decreased by reducing the number of hops. The peers in such network then can contribute with improve functionality and can perform well in P2P networks.