Virtualized Network Function Consolidation Based on Multiple Status Characteristics

Abstract

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.

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

“…Having reached a maximum by the end of the first decade of the years 2000, the attention on the theme has been slightly decreasing with the advent of network virtualization and softwarization brought forth by SDN, NFV, and the advent of 5G (with some notable exceptions, like [95,154,155], among others). As we noted in the Introduction, the reason can be attributed to the belief that the increased flexibility and the consolidation of resources made possible by virtualized networking components (see, e.g., [156][157][158]) would have sufficed not only to compensate the increased energy consumption caused by the adoption of general-purpose hardware, but also to curb it in general. We noticed that this is not the case.…”

Application of KNCMA Algorithm Integrating Network Features in Data Resource Scheduling

“…The simulation results indicate that the proposed method has higher efficiency in terms of energy, bandwidth, migration cost, etc. [17]. Blocking the line head at the current bandwidth receiver can affect overall network performance.…”

Energy Efficiency Through Joint Routing and Function Placement in Different Modes of SDN/NFV Networks

“…A resource allocation problem, considering constraints on delay, link utilization, and server utilization, is presented in [30], which enables energy-aware SFC for SDN-based networks. In order to achieve the trade-off among energy saving, bandwidth usage minimization, and migration cost reduction, the authors of [31] propose a VNF consolidation method (VCMM), which determines appropriate servers to be turned off by leveraging neural network with multiple characteristics of network status as input. VCMM migrates VNFs on servers to turn them off by adopting a greedy mechanism.…”