Virtual Network Function Placement Algorithm with Near-Optimal Energy Efficiency

PROJECT TITLE :

Near-Optimal Energy-Efficient Algorithm for Virtual Network Function Placement

ABSTRACT:

Network Function Virtualization, or NFV, is a relatively new NetWorking technology that was developed in the hope that it will one day be able to support more complex and diverse types of network services. NFV's most distinguishing characteristic is that it can decouple network functions from the actual hardware they run on. Within the framework of the NFV architecture, numerous Virtual Network Functions (VNFs) of varying kinds are installed on various software-based middleboxes by telecom providers. The term "Service Function Chain" refers to the order in which a series of "Virtual Network Functions," or VNFs, are executed. Traffic moves through this chain in the order specified (SFC). However, there is still a question mark over how to most efficiently position VNFs in various locations and direct SFC requests while simultaneously cutting down on energy consumption. As a result, we are looking into the possibility of jointly optimizing the placement of VNFs and the traffic steering in telecom networks in order to maximize energy efficiency. Following the presentation of the model for power consumption in NFV-enabled telecom networks, we proceed to the formulation of the studied problem as an Integer Linear Programming (ILP) model. We design a polynomial algorithm that, based on the Markov approximation technique, is capable of achieving near-optimal performances in light of the fact that it has been established that the problem is NP-hard. In addition, our algorithm can be adapted to an online version in order to accommodate requests for SFC that are received in a dynamic fashion. The long-term averaged performance of the online algorithm comes extremely close to being optimal. Extensive simulation results show that in comparison to the benchmark algorithms, our algorithm can reduce the power consumption of telecom networks by up to 14.08 and 13.72 percent, respectively, in both the offline and online scenarios. These findings come from the simulations.

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