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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.

Software-defined networking (SDN) technology enables us to flexibly configure switches in a network. Previously, distributed SDN control methods have been discussed to improve their scalability and robustness. Distributed placement of controllers and backing up each other enhance robustness. However, these techniques do not include an emergency measure against large-scale failures such as network separation induced by disasters. In this study, we first propose a network partitioning method to create a robust control plane (C-Plane) against large-scale failures. In our approach, networks are partitioned into multiple sub-networks based on robust topology coefficient (RTC). RTC denotes the probability that nodes in a sub-network isolate from controllers when a large-scale failure occurs. By placing a local controller onto each sub-network, 6%-10% of larger controller-switch connections will be retained after failure as compared to other approaches. Furthermore, we discuss reactive emergency reconstruction of a distributed SDN C-plane. Each node detects a disconnection to its controller. Then, C-plane will be reconstructed by isolated switches and managed by the other substitute controller. Meanwhile, our approach reconstructs C-plane when network connectivity recovers. The main and substitute controllers detect network restoration and merge their C-planes without conflict. Simulation results reveal that our proposed method recovers C-plane logical connectivity with a probability of approximately 90% when failure occurs in 100 node networks. Furthermore, we demonstrate that the convergence time of our reconstruction mechanism is proportional to the network size.

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