Search

The survival of peer-to-peer systems depends on the contribution of resources by all the participating peers. Selfish behavior of some peers that do not contribute resources inhibits the expected level of service delivery. Free riding has been found to seriously affect the performance and negates the sharing principle of peer-to-peer networks. In this paper, first, we investigate through simulations the effectiveness of a proposed linear model for mitigating free riding in a P2P system. Second, we extended the initial linear model by incorporating additional constraints on download and upload of each peer. This helps in reducing the effects of free riding behavior on the system. Lastly, we evaluate the impacts of some parameters on the models. [ABSTRACT FROM AUTHOR]

The existence and performance of peer-to-peer systems depend on the contribution of resources from interacting peers. One of the challenges of resource sharing in peer-to-peer systems is free riding. A situation users attempt to exploit the system by utilizing the resources of others without contributing. We view this from rationality perspective that every peer in the network will attempt to maximize their utility of the system. In this paper, we approach the problem of free riders mitigation from utility optimization point of view, by modeling each peer's interest as Utility Maximization Problem (UTP). We propose analytical model for the whole network as a mixed integer linear programming model. The super peers in the network are given the responsibility of maximizing the utility of all peers connected to them. This is to ensure fairness among the interacting peers and the stability of the entire system. This technique allows peers to either upload or download resources based on their best strategy and interest. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study is to determine the genetic relatedness of extended-spectrum beta-lactamases (ESBL)-producing Escherichia coli using the enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) technique., Methods: Suspected Gram-negative bacteria with their identities from the clinical samples were confirmed using Microgen GN-A-ID Kit. The double-disc synergy test was used to confirm for ESBL-producing E. coli . The susceptibility of the organisms was tested against eleven antimicrobial agents. A singleplex PCR assay was carried out targeting TEM, SHV, CTX-M, and OXA. ERIC-PCR performed, and band patterns obtained were visually evaluated. A dendrogram of the ERIC-PCR fingerprint pattern was done with the aid of DendroUPGMA using the cluster method., Results: Of the 576 clinical samples collected, 23 isolates were confirmed E. coli , and all (100%) are ESBL producers. The highest antibiotic resistance rate was recorded in cefixime (95.6%), and the least was amikacin (17.4%). The predominant ESBL gene is bla TEM genes (95.6%). Gel analysis of ERIC-PCR revealed 1-6 bands. The profiles of the ERIC-PCR differentiated the 23 E. coli isolates into four ERIC cluster types., Conclusion: More than 80% of the isolates are sensitive to amikacin, with greater than 95% harboring bla TEM genes. Overall, ERIC obtained from the clinical specimens indicated some evidence in the genetic relatedness of the ESBL genes among E. coli isolates., (Copyright: © International Journal of Health Sciences.)