Jianguo Ding

Summary. With the growth in size, heterogeneity, pervasiveness, and complexity of applications and network services, the effective management of distributed sys-tems has become more important and more difficult. Due to the inherent complexity of large-scale distributed systems, it is ...

Email categorization is challenging due to its sparse and noisy feature space. To address this problem, a novel semantic Vector Space Model (sVSM) using WordNet is proposed in this paper. The basic idea of sVSM is to select related semantic features that will increase the global information, and use them to enrich the semantic feature of an email. The proposed categorization method based on sVSM creates the sementic feature of an email category by both extracting terms of training email and enriching these terms with their concept-chains in WordNet. Next, tf*iwf*iwf algorithm is used to adjust the weight of the semantic feature vector. Experimental evaluations show that the proposed categorization method categorizing emails better than other email categorization methods based on traditional VSM, Baysian and KNN. More experiments show the proposed categorization method yielding better accuracy for smaller training sets with highlighting the semantic feature during identifying an email category.

As demands for higher data rates increase, DSL systems become incapable of keeping up due to the electromagnetic coupling present in the binders of the telephone lines. This electromagnetic coupling, known as crosstalk, is several orders greater in magnitude than the background noise. The reported techniques focusing on completely removing the crosstalk usually lead to computationally intensive solutions which may be infeasible to implement with existing hardware. For this reason, partial crosstalk removal has been proposed. In this work, we investigate the capability of the partial crosstalk cancellation for the fair or equal rate balancing among the users, subject to a constrained computational resource. Approaches which expend the computational resources in order to achieve fair or equal rates among the users were developed. Specifically, the proposed efficient Max-Min algorithm which is based on a dual optimization framework has a high convergence speed and low complexity for deployment in the xDSL systems.

This paper discusses some of the key points related to Standardization that need to be noted and discussed in the wide ICT community, to help understand the implication and impact of emerging and future network technologies on the various stakeholders of the global ICT ecosystems. The implication of emerging and future network technologies is that it is now the time to involve every key stakeholder in shaping, reviewing and contributing to the evolution of standards as well as the processes involved in making standards. The success to attaining quality and stability of emerging and future standards hinges on the engagements of the various key stakeholders and roles discussed in this paper. This paper is aimed at helping various stakeholders identify where they fit in the changing landscape of standardization, and promoting further discussions and debates along the topic, since the necessary discussions are not taking place at the pace expected by the global ICT community. We look at the key aspects requiring continued discussions and consideration so as to help accelerate standards development processes and adoption of standards, to help facilitate innovation and new technologies that dramatically bring about changes to the global economy and improve the lives of citizens across the globe. Therefore, it is very important to understand that Future Network Research directions must be aligned with newly launched activities in standardization of emerging and future network technologies.

In this paper, we report on some perspectives we give on how to create a viable Evolution Path towards Self-Managing Future Internet via a “standardizable" and commonly-shared architectural Reference Model for Autonomic Network Engineering and Self-Management. We present a Scenario on how the Self-Managing Future Internet can be developed via a viable Evolution Path that starts with today's network models, architectures, protocols such as IPv6 (in particular) and paradigms. The scenario then goes on to define the incremental changes and concepts necessitated and guided by a unified, holistic, commonly-shared, architectural Reference Model for Autonomic Network Engineering and Self-Management that needs to be developed and standardized first, as a starting point to creating the Evolution Path towards the Self-Managing Future Internet. This evolution of today's network models, architectures, networking paradigms and protocols such as IPv6 (towards IPv6++) must be guided and necessitated by the architectural Reference Model. The Scenario is a “what-if” type of Scenario that presents solid and realistic steps that define an evolutionary roadmap to achieving a very advanced feature-rich Self-Managing Future Internet by 2015, which can continue to evolve beyond that time frame. The ongoing activities of the EC funded FP7-EFIPSANS Project (http://www.efipsans.org/) are geared towards this goal.

Bayesian networks have been used widely in modelling complex network systems. Probabilistic inferences are normal requirements in control and management of networks, particularly in modelling diagnosis systems for large and complex networks in probabilistic and dynamic environments. This paper provides a random simulation mechanism to construct the simulation in Bayesian networks for probabilistic inference, so that the simulation in Bayesian networks is close to real life networks and further the intelligent decision in management of networks can be obtained

As networks grow in size, heterogeneity, and complexity of applications and network services, an efficient network management system needs to work effectively even in face of incomplete management information, uncertain situations and dynamic changes. We use Bayesian networks to model the network management and consider the probabilistic backward inference between the managed entities, which can track the strongest causes and trace the strongest routes between particular effects and its causes. This is the foundation for further intelligent decision of management in networks.

The growing complexity of IP networks in terms of hardware components, operating system, communication and application software and the huge amount of dependencies among them have caused an increase in demand for network management systems, particularly in fault management An efficient fault detection system needs to work effectively even in face of incomplete management information, uncertain situations and dynamic changes. In this paper, dynamic Bayesian networks are proposed to model static and dynamic dependencies between managed objects in IP networks. Prediction strategies and a backward inference approach are provided for the proactive management in fault detection based on the dynamic changes of IP networks.

The growing complexity of distributed systems in terms of hardware components, operating system, communication and application software and the huge amount of dependencies among them have caused an increase in demand for distributed management systems. An efficient distributed management system needs to work effectively even in face of incomplete management information, uncertain situations, and dynamic changes. In this paper, Bayesian networks are proposed to model dependencies between managed objects in distributed systems. The strongest dependency route (SDR) algorithm is developed for backward inference in Bayesian networks. The SDR algorithm can track the strongest causes and trace the strongest routes between particular effects and its causes, the strongest dependency of causes can be also achieved by the algorithm. Thus, the backward inference provides an efficient mechanism in fault locating, and is beneficial for performance management.

When a complex information system is modelled by a Bayesian network the backward inference is normal requirement in system management. This paper proposes one inference algorithm in Bayesian networks, which can track the strongest causes and trace the strongest routes between particular effects and their causes. This proposed algorithm will become the foundation for further intelligent decision in management of information systems.

The level of seriousness and sophistication of recent cyber-attacks has risen dramatically over the past decade. This brings great challenges for network protection and the automatic security management. Quick and exact localization of intruder by an efficient intrusion detection system (IDS) will be great helpful to network manager. In this paper, Bayesian networks (BNs) are proposed to model the distributed intrusion detection based on the characteristic of intruders’ behaviors. An inference strategy based on BNs are developed, which can be used to track the strongest causes (attack source) and trace the strongest dependency routes among the behavior sequences of intruders. This proposed algorithm can be the foundation for further intelligent decision in distributed intrusion detection.

In real-life distributed systems, dynamic changes are unavoidable properties because of the degeneration or improvement in system performance. Hence to understand the dynamic changes and to "catch the trend" of the changes in distributed systems will be very important for distributed systems management. In order to model the dynamic changes in distributed systems, temporal extensions of Bayesian networks are employed to address the temporal factors and to model the dynamic changes of managed entities and the dependencies between them. Furthermore, the prediction capabilities are investigated by means of the relevant inference techniques when the imprecise and dynamic management information occurs in the distributed system

With the proliferation of novel paradigms in distributed systems, including service-oriented computing, ubiquitous computing or self-organizing systems, an efficient distributed management system needs to work effectively even in face of incomplete management information, uncertain situations and dynamic changes. In this paper, Bayesian networks are proposed to model dependencies between managed objects in distributed systems management. Based on probabilistic backward inference mechanisms the so-called Strongest Dependency Route (SDR) algorithm is used to compute the set of most probable faults that may have caused an error or failure.

This investigation was performed to see the regularity of parameters in the modified mixture of experts (MoE) to direct parameter selection in real applications. The experiment was performed on the mean of monthly precipitation in May from 1956a through 2004a in a Chinese region. We got stable parameter combinations through testing on various combinations of parameters in various value ranges, and made statistics on the parameters in the stable combinations. The results showed that all the three parameters follow certain laws and suggest their importance. The relationship between the fuzzy weighting exponent and the value ranges of the cluster number and the index of the effectiveness factor are linear. It is of guiding significance to parameter selection in MoE systems.