Resource Provisioning in Fog Computing - A Survey

Title

Resource Provisioning in Fog Computing - A Survey

Creator

Vetriveeran, Divya; Sri, Leena R.

Description

The internet world has created an era where any device can interconnect with each other. Gathering intelligence from streaming data is challenging and can create wonders and valuable innovations for humanity. The shortcomings of connectivity due to the remote location of the cloud induce latency and performance issues in real-time. Thus, a traditional cloud may not be suitable for all applications. A secure, low latent bandwidth infrastructure under research led to Fog Computing. The fog nodes have limited resources, and effective utilization can boost the applications performance. Ensuring effective routing of the tasks and load balancing among the nodes is essential and tedious in any network. Resource management becomes challenging due to heterogeneity, dynamic workload, unpredictability of the computing environment, and so on. In such cases, using Artificial Intelligence (AI) can be promising, provided the complexity and the computing are handled. Proactive load handling based on the changes in network traffic has a huge scope for research. This article gives a detailed survey of the various fog network architectures and the intelligent methodologies in resource allocation in a fog network using machine learning algorithms. Furthermore, the article shows the directions of research in intelligent resource allocation and handling. 2025 Copyright held by the owner/author(s)

Source

ACM Computing Surveys;Volume;57;Issue;12;Article No.;305;

Date

01-01-2025

Publisher

Association for Computing Machinery

Subject

artificial intelligence algorithms; Fog computing; mi-croservices; research challenges; resource provisioning; software defined networking

Coverage

Vetriveeran D., AIML & Data Science, Christ University, Bangalore, India; Sri L.R., Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, India

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 3600300; CODEN: ACSUE

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.1145/3744662

https://www.scopus.com/pages/publications/105011388077?origin=resultslist