What is it about?
Network virtualisation is an important component of future internet architecture. Optimal allocation of network resources in terms of utilisation, quality of service and energy consumption has been a challenge. We formulate a multi-objective virtual network embedding problem with three objectives: resource saving, energy saving and congestion avoidance. We present the modelling techniques and solution approaches that use both the weighting method and constraint method to search for the pareto-optimal solutions that produce the best compromised solutions for three objectives.
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Why is it important?
In a network virtualisation environment, multiple virtual networks coexist in the same substrate network by virtue of virtualisation of substrate resources. Most of the State-of-the-Arts virtual network embedding focus on single objective such as minimising cost, maximising the overall revenue... Clearly, it is not satisfactory as the solution for one, such as congestion, may have negative impacts on other desire outcomes such as energy consumption, revenue...for network and service providers. In fact, single-objective virtual network embedding leads to frequent ad-hoc adjustment and reconfiguration, which are resource-expensive and interfere with current activities on virtual networks. Realistic solutions require optimisation on multi-objective concurrently.
Perspectives
Writing this article is a great pleasure as it has co-authors with whom I have had long standing collaborations. This article also leads to the application of multi-objective optimisation in virtual network embedding, and the modelling techniques and solution approaches of weighting method and constraint method for the pareto-optimal solutions for the best compromised solutions for multi-objectives.
Minh Pham
University of Technology Sydney
Read the Original
This page is a summary of: Congestion-Aware and Energy-Aware Virtual Network Embedding, IEEE/ACM Transactions on Networking, February 2020, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tnet.2019.2958367.
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