Routing-Led Evolutionary Algorithm for Large-Scale Multi-Objective VNF Placement Problems
About
Modern data centers contain thousands of servers making them major consumers of electricity. To minimize their environmental impact, it is critical that we use their resources efficiently. In this paper we study how to discover the optimal placement of virtual network functions in large scale data centers. We propose a novel parallel metaheuristic, fast heuristic objective functions of the QoS and new memory efficient data structures for large networks. We further identify a simple, fast heuristic that can produce competitive solutions to very large problem instances. Using these new concepts, we are able to find high quality solutions for data centres with up to 64,000 servers.
Peili Mao, Joseph Billingsley, Wang Miao, Geyong Mi, Ke Li• 2025
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Multi-Objective Optimization | Network Topologies 500 servers | Relative Wall Clock Time1 | 5 | |
| Multi-Objective Optimization | Network Topologies 1000 servers | Relative Median Wall Clock Time1 | 5 | |
| Multi-Objective Optimization | Network Topologies 2000 servers | Relative Median Wall Clock Time1 | 5 | |
| Multi-Objective Optimization | Network Topologies 4000 servers | Relative Median Wall Clock Time1 | 5 | |
| Multi-Objective Optimization | Network Topologies 8000 servers | Relative Median Wall Clock Time1 | 5 | |
| Multi-Objective Optimization | Network Topologies 16000 servers | Relative Median Wall Clock Time1 | 5 |
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