Routing-Led Evolutionary Algorithm for Large-Scale Multi-Objective VNF Placement Problems

2025-12-17

AI Summary

This work addresses the multi-objective virtual network function (VNF) deployment problem in hyperscale data centers (64,000 servers), jointly optimizing QoS assurance, energy consumption reduction, and resource utilization. We propose a routing-guided parallel multi-objective evolutionary algorithm framework, which innovatively integrates a fast QoS-aware heuristic evaluation model, a memory-efficient compact graph representation, and lightweight heuristic strategies. The method significantly improves computational efficiency and scalability, enabling stable generation of high-quality Pareto-optimal solution sets on networks with tens of thousands of nodes. Compared to state-of-the-art approaches, it achieves substantial improvements in both convergence speed and solution quality—reducing runtime by up to one order of magnitude while enhancing hypervolume and spread metrics. Our framework provides a practical, scalable optimization paradigm for large-scale network function virtualization deployment, validated under realistic infrastructure constraints and traffic patterns.

Technology Category

• Search and Optimization: Evolutionary Computation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Optimizes virtual network function placement in large data centers
• Reduces electricity consumption through efficient resource utilization
• Solves scalability for up to 64,000 servers with new algorithms

Innovation

• Parallel metaheuristic for VNF placement optimization
• Fast heuristic functions for QoS and memory efficiency
• Scalable solutions for up to 64,000 server data centers