Large-scale P2P Applications Research Articles

A self-similar super-peer overlay construction scheme for super large-scale P2P applications

Unstructured peer-to-peer (P2P) overlay networks with two-layer hierarchy, comprising an upper layer of super-peers and an underlying layer of ordinary peers, are used to improve the performance of large-scale P2P applications like content distribution and storage. In order to deal with continuous growth of participating peers, a scalable and efficient super-peer overlay topology is essential. However, there is relatively little research conducted on constructing such super-peer overlay topology. In the existed solutions, the number of connections required to be maintained by a super-peer is in direct proportion to the total number of super-peers. For super large-scale P2P applications, i.e. the number of participating peer is over 1,000,000, these solutions are not scalable and impractical. Therefore, in this paper, we propose a scalable hierarchical unstructured P2P system in which a self-similar square network graph (SSNG) is proposed to construct and maintain the super-peer overlay topology adaptively. The SSNG topology is a constant-degree topology in which each node maintains a constant number of neighbor nodes. Moreover, a simple and efficient message forwarding algorithm is presented to ensure each super-peer to receive just one flooding message. The analytical results showed that the proposed SSNG-based overlay is more scalable and efficient than the perfect difference graph (PDG)-based overlay proposed in the literature.

Scalable Feedback Aggregating (SFA) Overlay for Large-Scale P2P Trust Management

In Peer-to-Peer (P2P) trust management, feedback provides an efficient and effective way to build a reputation-based trust relationship among peers. There is no doubt that the scalability of a feedback aggregating overlay is the most fundamental requirement for large-scale P2P computing. However, most previous works either paid little attention to the scalability of feedback aggregating overlay or relied on the flooding-based strategy to collect feedback, which greatly affects the system scalability. In this paper, we proposed a scalable feedback aggregating (SFA) overlay for large-scale P2P trust evaluation. First, the local trust rating method is defined based on the time attenuation function, which can satisfy the two dynamic properties of trust. The SFA overlay is then proposed from a scalable perspective. Not only can the SFA overlay strengthen the scalability of the feedback aggregation mechanism for large-scale P2P applications, but it can also reduce networking risk and improve system efficiency. More importantly, based on the SFA overlay, an adaptive trustworthiness computing method can be defined. This method surpasses the limitations of traditional weighting methods for trust factors, in which weights are assigned subjectively. Finally, the authors design the key techniques and security mechanism to be simple in implementation for the easy incorporation of the mechanism into the existing P2P overlay network. Through theoretical and experimental analysis, the SFA-based trust model shows remarkable enhancement in scalability for large-scale P2P computing, as well as has greater adaptability and accuracy in handling various dynamic behaviors of peers.

SDIPR: A Generic P2P Semantic Distance Inference Approach Based on Spring Relaxation System

Large-scale P2P applications can benefit from the ability to predict semantic distances to other peers without having to contact them first. In this paper, we propose a generic semantic distance inference scheme, SDIPR, in P2P network, which, through measuring semantic distances from peers to a handful of other peers and the current coordinates of those peers, assigns synthetic coordinates to each peer, and can approximately predict the semantic distance between any two peers. Specifically, in our paper, the semantic distance between peers is quantitatively characterized through vector space model based on peers’ semantic profiles and weighted with the number of documents in each peer. Then, we adopt the spring relaxation method, mimicking the physical massspring system, to simulate the semantic embedding procedure, which can find minimal energy configuration corresponding to relatively accurate semantic embedding. Simulation results show that a 3-dimension Euclidean model can embed these peers with relatively high accuracy. Moreover, we compare SDIPR with the influential existing Locality Sensitive Hashing (LSH) based multi-dimension indexing approaches, and show that SDIPR performs much better than one index scheme (that is, one group of hash functions with same dimensions as our approach), and are comparable with LSH-based schemes using three indexes (total 9 dimensions).