No abstract

SUMMARYThe availability of IP networks has increased its importance due to the evolving use of real-time and mission-critical applications on IP networks. Methods for preparing alternate routing tables that can be used for fast restoration from link failures have been investigated. In such methods, each node has to compute a number of alternate routing tables in advance since they have to prepare for each potential failure. The resulting huge number of alternate routing tables has prevented these methods from being deployed. In this paper, we propose a method for reducing the number of alternate routing tables for link failure. It analyzes three types of shortest path trees on the basis of link-state information. We show that the number of alternate routing tables can be reduced to 1/100, on average, from that with the conventional method, and that they are small enough to be stored in the memory of IP routers.

No abstract

No abstract

Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.