Application Components Migration in NFV-based Hybrid Cloud/Fog Systems

TL;DR

This paper addresses the challenge of migrating application components in NFV-based hybrid cloud/fog systems to reduce latency caused by fog node mobility, proposing a heuristic solution that improves application makespan and migration efficiency.

Contribution

It introduces a mathematical model and a heuristic algorithm for optimal component migration considering fog node mobility in hybrid cloud/fog NFV environments.

Findings

The heuristic reduces application makespan compared to random migration.

The proposed method decreases the number of migrations needed.

Results demonstrate improved performance over baseline approaches.

Abstract

Fog computing extends the cloud to the edge of the network, close to the end-users enabling the deployment of some application component in the fog while others in the cloud. Network Functions Virtualization (NFV) decouples the network functions from the underlying hardware. In NFV settings, application components can be implemented as sets of Virtual Network Functions (VNFs) chained in specific order representing VNF-Forwarding Graphs (VNF-FG). Many studies have been carried out to map the VNF-FGs to cloud systems. However, in hybrid cloud/fog systems, an additional challenge arises. The mobility of fog nodes may cause high latency as the distance between the end-users and the nodes hosting the components increases. This may not be tolerable for some applications. In such cases, a prominent solution is to migrate application components to a closer fog node. This paper focuses on application component migration in NFV-based hybrid cloud/fog systems. The objective is to minimize the aggregated makespan of the applications. The problem is modeled mathematically, and a heuristic is proposed to find the sub-optimal solution in an acceptable time. The heuristic aims at finding the optimal fog node in each time-slot considering a pre-knowledge of the mobility models of the fog nodes. The experiment's results show that our proposed solution improves the makespan and the number of migrations compared to random migration and No-migration.