Priority-Queue based Dynamic Scaling for Efficient Resource Allocation in Fog Computing

TL;DR

This paper introduces a priority-queue based fog computing architecture with dynamic scalability, effectively reducing delay and power consumption for delay-sensitive applications by intelligently allocating resources based on task priority and load.

Contribution

It presents a novel fog computing framework that combines priority queues with dynamic device scaling to optimize delay and power efficiency.

Findings

Achieves 14.5% lower power consumption compared to existing schemes.

Reduces delay for delay-sensitive and -insensitive tasks.

Demonstrates improved resource allocation efficiency.

Abstract

In this emerging world of connected devices, the need for more computing devices with a focus on delay-sensitive application is critical. In this paper, we propose a priority-queue based Fog computing architecture combined with dynamic scalability of fog devices, which not only reduces the delay experienced by delay-sensitive tasks by categorizing the delay-sensitive and delay-insensitive tasks, but also dynamically allocates the fog devices within the network depending upon the computation load for reducing the power consumption. The results show that the proposed algorithm is able to achieve a significantly lower delay for both delay-sensitive and -insensitive tasks when compared with other related schemes with a 14.5% lower power consumption.