The increasing proliferation of Cloud Services (CSs) has made the reliable CS selection problem a major challenge. To tackle this problem, this article introduces an effective trust model called Chain Augmented Naïve Bayes-based Trust Model (CAN-TM). This model leverages the correlation that may exist among QoS attributes to solve many issues in reliable CS selection challenge such as predicting missing assessments, and improving accuracy of trust computing. This is achieved by combining both the n-gram Markov model and the Naïve Bayes model. Experiments are conducted to validate that our proposed CAN-TM outperforms state-of-the-art approaches.
In a crowdsourcing system, it is important for the crowdsourcer to engineer extrinsic rewards to incentivize the participants. In this paper, we incorporate network effects as a contributing factor to intrinsic rewards, and study its influence on the design of extrinsic rewards. We show that the number of participating users and their contributions to the crowdsourcing system evolve to a steady equilibrium. We design progressively more sophisticated extrinsic reward mechanisms, and propose new and optimal strategies for a crowdsourcer to obtain a higher utility. Through simulations, we demonstrate that with our new strategies, a crowdsourcer can attract more participants.
In this paper, we make an in-depth observation of practical Internet datasets, and investigate the relationship between betweenness centrality and network throughput. Furthermore, we propose a new routing observation factor, di?erential-ratio-of-betweenness-centrality (DRBC), to denote the varying amplitude of betweenness centrality to node degree. We find an interesting phenomenon that DRBC is proportional to the routing efciency when the maximum betweenness centrality varies in a small range. Based on this, a DRBC-based routing scheme is proposed to improve network throughput. The experimental results verify that DRBC-based routing can improve the routing efciency and accelerate the routing optimization.
Introduction to the Special Section on Advances in Internet-Based Collaborative Technologies
Online social network services (OSNSs) are changing the fabric of our society, impacting almost every aspect of it. Over the last decades, multiple competing OSNSs have emerged. As a result, users are trapped in the walled gardens of their OSNS, encountering restrictions about the people they can interact with. Our work aims at enabling users to meet and interact beyond the boundary of their OSNSs. We introduce USNB -Universal Social Network Bus which revisits the "service bus" paradigm to address the requirements of social interoperability.
We propose PTP-MF (Pairwise Trust Prediction through Matrix Factorization), an algorithm to predict the intensity of trust/distrust relations in Online Social Networks. The PTP-MF algorithm maps each user i onto two low-dimensional vectors, namely, the trustor profile (describing her/his inclination to trust others) and the trustee profile (modelling how others perceive i as trustworthy) and computes the trust j places in j as the dot product of trustor profile of i and the trustee profile of j. Experiments indicate that the PTP-MF algorithm is more accurate than the state-of-the-art approaches (up to 9.65%) and scales well on real life graphs
Various differentiated pricing schemes have been proposed for the Internet market. Aiming at replacing the traditional single-class pricing for better welfare, yet, researchers have shown that existing schemes can bring only marginal profit gain for the ISPs. We point out that a proper form of differentiated pricing for the Internet should not only consider congestion, but also provide application specific treatment to data delivery. Formally, we propose an ?application-driven pricing? approach. Opposite from previous studies, we show that the revenue gain of multi-class pricing under our scheme can be significant. We also identify key factors that impact the revenue gain.