Energy Efficient Resource Allocation in Federated Fog Computing Networks

TL;DR

This paper presents an energy-efficient resource allocation strategy for federated fog computing networks using PON connectivity and MILP optimization to reduce power consumption significantly.

Contribution

It introduces a novel MILP model for VM placement in federated fog networks that minimizes power use while accounting for inter-VM traffic.

Findings

Power consumption reduced by up to 52% with the proposed model.

Effective VM placement improves energy efficiency in federated fog networks.

Utilization of PON enhances connectivity and resource sharing among fog units.

Abstract

There is a continuous growth in demand for time sensitive applications which has shifted the cloud paradigm from a centralized computing architecture towards distributed heterogeneous computing platforms where resources located at the edge of the network are used to provide cloud-like services. This paradigm is widely known as fog computing. Virtual machines (VMs) have been widely utilized in both paradigms to enhance the network scalability, improve resource utilization, and energy efficiency. Moreover, Passive Optical Networks (PONs) are a technology suited to handling the enormous volumes of data generated in the access network due to their energy efficiency and large bandwidth. In this paper, we utilize a PON to provide the connectivity between multiple distributed fog units to achieve federated (i.e. cooperative) computing units in the access network to serve intensive demands. We propose a mixed integer linear program (MILP) to optimize the VM placement in the federated fog computing units with the objective of minimizing the total power consumption while considering inter-VM traffic. The results show a significant power saving as a result of the proposed optimization model by up to 52%, in the VM-allocation compared to a baseline approach that allocates the VM requests while neglecting the power consumption and inter-VMs traffic in the optimization framework.