QoE Based Revenue Maximizing Dynamic Resource Allocation and Pricing for Fog-Enabled Mission-Critical IoT Applications

TL;DR

This paper proposes a statistically optimal, real-time dynamic resource allocation and pricing framework for fog-enabled mission-critical IoT applications, enhancing QoE and revenue for cloud service providers by efficiently managing stochastic request arrivals.

Contribution

It introduces a novel QoE-based pricing policy and an optimal dynamic allocation rule tailored for fog computing in IoT, outperforming static schemes.

Findings

Significant QoE and revenue improvements over benchmark schemes

The proposed method is statistically optimal and implementable in real-time

Simulation results validate the effectiveness of the framework

Abstract

Fog computing is becoming a vital component for Internet of things (IoT) applications, acting as its computational engine. Mission-critical IoT applications are highly sensitive to latency, which depends on the physical location of the cloud server. Fog nodes of varying response rates are available to the cloud service provider (CSP) and it is faced with a challenge of forwarding the sequentially received IoT data to one of the fog nodes for processing. Since the arrival times and nature of requests is random, it is important to optimally classify the requests in real-time and allocate available virtual machine instances (VMIs) at the fog nodes to provide a high QoE to the users and consequently generate higher revenues for the CSP. In this paper, we use a pricing policy based on the QoE of the applications as a result of the allocation and obtain an optimal dynamic allocation rule based on the statistical information of the computational requests. The developed solution is statistically optimal, dynamic, and implementable in real-time as opposed to other static matching schemes in the literature. The performance of the proposed framework has been evaluated using simulations and the results show significant improvement as compared with benchmark schemes.