Multimedia Services Placement Algorithm for Cloud-Fog Hierarchical Environments

TL;DR

This paper proposes a Cloud-Fog computing environment with predictive models and a placement algorithm to optimize multimedia service deployment, reducing latency, energy consumption, and costs.

Contribution

It introduces a novel environment model, predictive demand models, and a traffic-aware placement algorithm for multimedia services in hierarchical Cloud-Fog systems.

Findings

The algorithm effectively selects nodes closer to users, reducing latency.

Predictive models improve resource allocation and service quality.

The approach decreases energy consumption by turning off unnecessary servers.

Abstract

With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud-Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes' storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy