Plan-Based Scalable Online Virtual Network Embedding

TL;DR

This paper introduces OLIVE, a scalable online algorithm for virtual network embedding in edge computing, which uses offline aggregated demand planning to efficiently handle highly dynamic and large-scale request loads.

Contribution

The paper presents OLIVE, a novel online VNE algorithm that leverages offline aggregated demand planning to significantly improve scalability and request handling capacity.

Findings

Handles two orders of magnitude more requests per second than previous methods.

Uses offline aggregated demand to guide online embedding decisions.

Suitable for large-scale, real-time edge network environments.

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.