Intelligent scaling for 6G IoE services for resource provisioning

Abdullah Alharbi; Hashem Alyami; Poongodi M; Hafiz Tayyab Rauf; Seifedine Kadry

doi:10.7717/peerj-cs.755

Intelligent scaling for 6G IoE services for resource provisioning

PeerJ Comput Sci. 2021 Oct 26:7:e755. doi: 10.7717/peerj-cs.755. eCollection 2021.

Authors

Abdullah Alharbi¹, Hashem Alyami², Poongodi M³, Hafiz Tayyab Rauf⁴, Seifedine Kadry⁵

Affiliations

¹ Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
² Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
³ College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
⁴ Department of Computer Science, Faculty of Engineering & Informatics, University of Bradford, Bradford, United Kingdom.
⁵ Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway.

Abstract

The proposed research motivates the 6G cellular networking for the Internet of Everything's (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.

Keywords: 5G; 6G enabled Internet of Everything; Data science; Deep learning; Intelligent scaling; Machine learning; Resource enhancement.

Grants and funding

This research was supported by Taif University Researchers Supporting Project number (TURSP- 2020/306), Taif University, Taif, Saudi Arabia. The funders had no role in data collection and analysis, decision to publish, or preparation of the manuscript. The funders did have a role in study design.