Representation of Categorical Specification of Self-Configurations in Reactive Autonomic Systems Framework

Software complexity crisis brings huge obstacle to further progress in IT industry. To alleviate this problem, researchers are asked to build systems which can benefit from automation. With autonomic behavior, the real-time reactive systems can be more self-managed and adaptive to their environment. However, most of current formal approaches fail to specify such kind of system. In this paper, we proposed an approach to formally specify reactive autonomic systems. First, we used category theory to formalize reactive autonomic systems;then we focused on the categorization of self-configurations and work flows of reactive autonomic systems, and finally we used XML to specify the categorical models. In doing so, it can help to build the foundation of reactive autonomic systems with autonomic features and verify emergent behaviors.

Graph theory based physical cell identity self-configuration for LTE-A network

Self-configuration of physical cell identity （PCI） is a key feature for the long term evolution advanced （LTE-A） network. The PCI is used to identify the different cells in the system, becoming an essential cell configuration parameter. Considering the uncoordinated deployments of the evolved Node B （eNodeB） and the limited number of PCI, the PCI assignment for cells would be quite complex. This paper presents and puts forward a graph theory based centralized PCI self-configuration scheme （GT-PCIS）. The PCI self-configuration problem is mapped to the well-known minimum spanning tree （MST） problem in order to optimize the PCI reuse distance and decrease the multiplexing interference throughout the entire network. The proposal provides a greedy search to make the locally optimal selection of PCI at each stage, and to achieve a global optimum. To demonstrate the algorithm validity, performances of GT-PCIS and manual configuration are evaluated. Simulation results show that the proposed GT-PCIS outperforms other configuration algorithms even under the condition of severe PCI deficiency.

Building up Multi－Layered Perceptrons as Classifier System for Decision Support

This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.

Subject Oriented Autonomic Cloud Data Center Networks Model

This paper investigates autonomic cloud data center networks, which is the solution with the increasingly complex computing environment, in terms of the management and cost issues to meet users’ growing demand. The virtualized cloud networking is to provide a plethora of rich online applications, including self-configuration, self-healing, self-optimization and self-protection. In addition, we draw on the intelligent subject and multi-agent system, concerning system model, strategy, autonomic cloud computing, involving independent computing system development and implementation. Then, combining the architecture with the autonomous unit, we propose the MCDN (Model of Autonomic Cloud Data Center Networks). This model can define intelligent state, elaborate the composition structure, and complete life cycle. Finally, our proposed public infrastructure can be provided with the autonomous unit in the supported interaction model.