Earticle

Software systems are widely spreading throughout and moving towards autonomic environment. Self-managing systems are designed so that they make adaptive decisions and are self-governing, self-directed and self-adapting. Self-managed systems are classified into basic requirements of self-configuration, self-protecting, self-healing and self-optimizing by IBM. Most of fact-findings has been taken out on implementation and benefits of self-managed systems. Some work on the evaluation of self-managed systems has been done. In this re-search work, seven metrics given by Claudia are applied on self-managed operating system proposed by previous researchers and induce a new monitoring module which will help in measuring the effectiveness of self-healing operating system.

All models of ubiquitous computing share a vision of small, inexpensive, robust networked processing devices, distributed at all scales throughout everyday life and generally turned to distinctly common-place ends. This paper presents a solution for Parctab Ubiquitous Computing experiment previously been studied

Cloud resource managers face many problems, such as the dynamic changes of incoming load and demand elasticity of resources. From the aspect of elastic configuration management technology of virtual resources, this paper focus on how to provide quick and reliable cloud resources for users. Virtual machine resources automatic configuration management technology is proposed in this paper, the reverse reinforcement learning technique is introduced into cloud virtual resource management, configuration management process of the virtual machine is modeled as a Markov decision model. According to the running state of the application system and the dynamic changes of the input load, this technology can make an automatic decision to add or remove a number of virtual machines. Experimental results show that this technology can complete the tasks of automating configuration of virtual resource management according to the changing load, respond to end user's in a timely manner, and ensure the SLA requirements of cloud users.

This paper partially presents a Hierachical neural network architecture for providing the intelligent control of complex Automatic Transmission(A/T) system which is usually nonlinear and hard to model mathematically. It consists of the module to apply or release an engine brake at the slope and that to judge the intention of the driver. The HNN architecture simplifies the structure of the overall system and is efficient for the learning time. This paper describes how the sub-neural networks of each module have been constructed and will compare the result of the intelligent A/T control to that of the conventional shift pattern.

This paper partially presents a Hierachical neural network architecture for providing the intelligent control of complex Automatic Transmission(AJT) system which is usually nonlinear and hard to model mathematically. It consists of the module to apply or release an engine brake at the slope and that to judge the intention of the driver. The HNN architecture simplifies the structure of the overall system and is efficient for the learning time. This paper describes how the sub-neural networks of each module have been constructed and will compare the result of the intelligent hJT control to that of the conventional shift pattern.