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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Oba Zubair Mustapha
Kwara State Polytechnic
Computer Science & IT Electrical & Electronics Engineering

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Service-aware LSP selection with fuzzy based packet scheduling scheme for non-real time traffics Oba Zubair Mustapha; Muhammad Ali; Yim Fun Hu; Raed A. Abd-Alhameed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i2.pp126-139

Abstract

An essential solution is available in Multi-protocol label switching (MPLS), which solve the problems faced by present-day networks: speed, scalability, quality-of-service (QoS) management, and traffic engineering. This paper is an extension of work on Fuzzy based Packet Scheduling Algorithm (FPSA) combined with Packets Processing Algorithm (PPA) in an Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) networks. This will make provision for an intelligent service to the Label Switched Path (LSP) in MPLS networks. Several research work have been proposed on the MPLS Traffic Engineering. However, it is still imperative to further research on the effect of bandwidth increment on the core network using different mechanisms such as the analytical model of MPLS, expert-based packet scheduling algorithm for MPLS QoS support. Since MPLS is not able to provide intelligent routing, it is necessary to propose an intelligent expert system of FPSA combined with PPA. And analytical model of packet forwarding in the MPLS network would be given to provide suitable solution to traffic congestion and reliable services. Furthermore, the network model created using Network Simulator (NS 2), which carries non-real time application such as File Transfer Protocol (FTP) with bandwidth variations. The results obtained from trace files are interpreted by AWK script and used for the further analysis.
Machine Learning Centered Energy Optimization In Cloud Computing: A Review Nomsa Puso; Tshiamo Sigwele; Oba Zubair Mustapha
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.5037

Abstract

The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing.
Co-Authors Muhammad Ali Nomsa Puso Raed A. Abd-Alhameed Tshiamo Sigwele Yim Fun Hu