Multilayer Resource-aware Partitioning for Fog Application Placement

TL;DR

This paper introduces a multilayer resource-aware partitioning approach for Fog computing that improves application placement efficiency by considering network topology and resource diversity, significantly increasing service placement and deadline satisfaction.

Contribution

It presents a novel multilayer network graph model for resource-aware partitioning that effectively handles resource heterogeneity and network topology in Fog environments.

Findings

Placed twice as many services as existing methods

Satisfies deadlines for three times more requests

Reduces resource wastage by up to 15-32 times

Abstract

Fog computing emerged as a crucial platform for the deployment of IoT applications. The complexity of such applications requires methods that handle the resource diversity and network structure of Fog devices while maximizing the service placement and reducing resource wastage. Prior studies in this domain primarily focused on optimizing specific application requirements and fail to address the network topology combined with the different types of resources encountered in Fog devices. To overcome these problems, we propose a multilayer resource-aware partitioning method to minimize the resource wastage and maximize the service placement and deadline satisfaction rates in a Fog infrastructure with high multi-user application placement requests. Our method represents the heterogeneous Fog resources as a multilayered network graph and partitions them based on network topology and resource features. Afterward, it identifies the appropriate device partitions for placing an application according to its requirements, which need to overlap in the same network topology partition. Simulation results show that our multilayer resource-aware partitioning method is able to place twice as many services, satisfy deadlines for three times as many application requests, and reduce the resource wastage by up to 15-32 times compared to two availability-aware and resource-aware methods.