Probabilistic Proximity-aware Resource Location in Peer-to-Peer Networks Using Resource Replication

Nowadays, content distribution has received remarkable attention in distributed computing researches and its applications typically allow personal computers, called peers, to cooperate with each other in order to accomplish distributed operations such as query search and acquiring digital contents. In a very large network, it is impossible to perform a query request by visiting all peers. There are some works that try to find the location of resources probabilistically (i.e. non-deterministically). They all have used inefficient protocols for finding the probable location of peers who manage the resources. This paper presents a more efficient protocol that is proximity-aware in the sense that it is able to cache and replicate the popular queries proportional to distance latency. The protocol dictates that the farther the resources are located from the origin of a query, the more should be the probability of their replication in the caches of intermediate peers. We have validated the proposed distributed caching scheme by running it on a simulated peer-to-peer network using the well-known Gnutella system parameters. The simulation results show that the proximity-aware distributed caching can improve the efficiency of peer-to-peer resource location services in terms of the probability of finding objects, overall miss rate of the system, fraction of involved peers in the search process, and the amount of system load

A Novel Non-Negative Matrix Factorization Method for Recommender Systems

Recommender systems collect various kinds of data to create their recommendations. Collaborative filtering is a common technique in this area. This technique gathers and analyzes information on users preferences, and then estimates what users will like based on their similarity to other users. However, most of current collaborative filtering approaches have faced two problems: sparsity and scalability. This paper proposes a novel method by applying non-negative matrix factorization, which alleviates these problems via matrix factorization and similarity. Non-negative matrix factorization attempts to find two non-negative matrices whose product can well approximate the original matrix. It also imposes non-negative constraints on the latent factors. The proposed method presents novel update rules to learn the latent factors for predicting unknown rating. Unlike most of collaborative filtering methods, the proposed method can predict all the unknown ratings. It is easily implemented and its computational complexity is very low. Empirical studies on MovieLens and Book-Crossing datasets display that the proposed method is more tolerant against the problems of sparsity and scalability, and obtains good results

Towards strategic bandwidth sharing in overlay multicast networks based on mechanism design theory

AbstractThe selfish behavior of the users in the overlay multicast networks can lead to degradation of the performance. In this paper, we target the mechanism design for the overlay networks based on the monopoly auction economies. In our proposed auction mechanism, the bandwidth of the service offered by the origin servers can be thought of as commodity. In this auction, the sellers are either the origin servers or the peers who forward the content to their downstream peers. Also, the corresponding downstream peers of each seller play the role of buyers who are referred to as bidders. Each bidder submits a sealed bid to its corresponding seller. The high bidder wins and pays its bid for the service. By theoretical and experimental analysis, we prove that the proposed auction mechanism achieves performance improvements in the overlay network