In network function virtualization (NFV), network function implemented in software on the generally shared servers is called virtualized network function (VNF). The characteristics of virtualization provide service function chain (SFC) provisioning with the required flexibility by allowing for placing VNFs anywhere and anytime. However, there may be a large number of servers with low utilization across the network at the low-load time, leading to enormous loss of energy. VNF consolidation, as an effective way to address this issue, allows for energy saving by consolidating VNFs into as fewer servers as possible. Unfortunately, it involves multiple conflicting goals, such as energy saving, bandwidth usage minimization, and migration cost reduction, which makes it hard to implement in practical environments. In order to achieve the tradeoff among these goals, this paper proposes a VNF consolidation method, VCMM, which determines appropriate servers to be turned off by leveraging neural network with multiple network status characteristics as input. The neural network is trained using the particle swarm optimization algorithm (PSO). VCMM migrates VNFs on servers to be turned off by adopting a greedy mechanism. Through extensive simulations, we demonstrate that the proposed VCMM outperforms prior approaches in terms of energy, bandwidth, and migration costs. INDEX TERMS Multiple status characteristics, network function virtualization (NFV), virtualized network function consolidation.
The characteristics of mobile Ad Hoc network make it face much more security threats than wired network, while the security of routing is the premise of secure application of Ad Hoc networks. In allusion to the safety weakness of resisting the active attacks from malicious nodes, trust metric is introduced to defend those attacks by loading a trust model on proposed Distance-Based LAR (DBALR). The improved Secure Trust-based LocationAided Routing (ST-LAR) algorithm utilizes direct trust and recommendation trust to prevent malicious nodes with low trust value from joining the forwarding. Simulation results reveal that ST-LAR can resist attacks from malicious nodes effectively; furthermore, it also performs better than DBLAR in terms of average end-to-end delay, packet successful delivery ratio and throughput.
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