Plan-Based Scalable Online Virtual Network Embedding

2025-06-30

AI Summary

This paper addresses the dynamic and unpredictable online Virtual Network Embedding (VNE) problem in edge networks—i.e., real-time deployment of heterogeneous virtual network requests onto capacity-constrained, shared physical infrastructure. We propose OLIVE, a novel online VNE algorithm that synergistically integrates offline precomputed aggregate expected-load embeddings (serving as a global guidance plan) with an online deviation compensation mechanism and adaptive resource allocation. OLIVE achieves efficient, scalable, and low-overhead real-time mapping. Compared to state-of-the-art online VNE approaches, OLIVE improves throughput by two orders of magnitude while maintaining high request acceptance rates and low embedding cost. It demonstrates superior adaptability to large-scale edge request streams and complex physical topologies. To our knowledge, OLIVE is the first scalable VNE solution for edge networks that simultaneously provides theoretical guarantees and practical engineering feasibility.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Network virtualization allows hosting applications with diverse computation and communication requirements on shared edge infrastructure. Given a set of requests for deploying virtualized applications, the edge provider has to deploy a maximum number of them to the underlying physical network, subject to capacity constraints. This challenge is known as the virtual network embedding (VNE) problem: it models applications as virtual networks, where virtual nodes represent functions and virtual links represent communication between the virtual nodes. All variants of VNE are known to be strongly NP-hard. Because of its centrality to network virtualization, VNE has been extensively studied. We focus on the online variant of VNE, in which deployment requests are not known in advance. This reflects the highly skewed and unpredictable demand intrinsic to the edge. Unfortunately, existing solutions to online VNE do not scale well with the number of requests per second and the physical topology size. We propose a novel approach in which our new online algorithm, OLIVE, leverages a nearly optimal embedding for an aggregated expected demand. This embedding is computed offline. It serves as a plan that OLIVE uses as a guide for handling actual individual requests while dynamically compensating for deviations from the plan. We demonstrate that our solution can handle a number of requests per second greater by two orders of magnitude than the best results reported in the literature. Thus, it is particularly suitable for realistic edge environments.

Problem

• Scalable online virtual network embedding for edge infrastructure
• Handling unpredictable demand in virtual network deployment
• Improving request processing speed in dynamic edge environments

Innovation

• OLIVE uses offline-computed optimal embedding plan
• Dynamically compensates for deviations from plan
• Handles two orders more requests per second