Smart grid: Bio-inspired algorithms energy distributions for data centers
The growing demand for data centre services has led to significant increases in data centre power consumption, highlighting the need for efficient power management strategies to ensure sustainable and energy-efficient operations. Virtualisation technology enables multiple virtual machines (VMs) to r...
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| Format: | Final Year Project / Dissertation / Thesis |
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2025
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| Online Access: | http://eprints.utar.edu.my/7246/1/fyp_CS_2025_WYH.pdf http://eprints.utar.edu.my/7246/ |
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| _version_ | 1854094496806993920 |
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| author | Woo, Yu Hang |
| author_facet | Woo, Yu Hang |
| author_sort | Woo, Yu Hang |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The growing demand for data centre services has led to significant increases in data centre power consumption, highlighting the need for efficient power management strategies to ensure sustainable and energy-efficient operations. Virtualisation technology enables multiple virtual machines (VMs) to run on a single physical server, improving resource sharing and utilisation. However, it also introduces challenges in optimising VM placement and migration to minimise power consumption while maintaining performance. This project proposes and evaluates three bio-inspired and evolutionary algorithms for VM allocation and migration: Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and a Modified Genetic Algorithm (MGA). These algorithms aim to reduce power consumption, improve resource utilisation, and enhance overall data centre efficiency. The system is implemented and simulated using the CloudSim Plus framework under both homogeneous and heterogeneous data centre environments. Four different workload scenarios were tested, and the performance of the three algorithms was compared against the data centre’s baseline VM allocation policy. Each scenario was executed 30 times to ensure the reliability and consistency of results. Simulation results demonstrate that all three proposed algorithms consistently achieved lower total power consumption across all servers compared to the baseline policy. These findings highlight the potential of bio-inspired VM allocation and migration strategies for improving energy efficiency and resource optimisation in modern data centres. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7246 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.72462025-12-29T10:18:50Z Smart grid: Bio-inspired algorithms energy distributions for data centers Woo, Yu Hang T Technology (General) The growing demand for data centre services has led to significant increases in data centre power consumption, highlighting the need for efficient power management strategies to ensure sustainable and energy-efficient operations. Virtualisation technology enables multiple virtual machines (VMs) to run on a single physical server, improving resource sharing and utilisation. However, it also introduces challenges in optimising VM placement and migration to minimise power consumption while maintaining performance. This project proposes and evaluates three bio-inspired and evolutionary algorithms for VM allocation and migration: Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and a Modified Genetic Algorithm (MGA). These algorithms aim to reduce power consumption, improve resource utilisation, and enhance overall data centre efficiency. The system is implemented and simulated using the CloudSim Plus framework under both homogeneous and heterogeneous data centre environments. Four different workload scenarios were tested, and the performance of the three algorithms was compared against the data centre’s baseline VM allocation policy. Each scenario was executed 30 times to ensure the reliability and consistency of results. Simulation results demonstrate that all three proposed algorithms consistently achieved lower total power consumption across all servers compared to the baseline policy. These findings highlight the potential of bio-inspired VM allocation and migration strategies for improving energy efficiency and resource optimisation in modern data centres. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7246/1/fyp_CS_2025_WYH.pdf Woo, Yu Hang (2025) Smart grid: Bio-inspired algorithms energy distributions for data centers. Final Year Project, UTAR. http://eprints.utar.edu.my/7246/ |
| spellingShingle | T Technology (General) Woo, Yu Hang Smart grid: Bio-inspired algorithms energy distributions for data centers |
| title | Smart grid: Bio-inspired algorithms energy distributions for data centers |
| title_full | Smart grid: Bio-inspired algorithms energy distributions for data centers |
| title_fullStr | Smart grid: Bio-inspired algorithms energy distributions for data centers |
| title_full_unstemmed | Smart grid: Bio-inspired algorithms energy distributions for data centers |
| title_short | Smart grid: Bio-inspired algorithms energy distributions for data centers |
| title_sort | smart grid: bio-inspired algorithms energy distributions for data centers |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/7246/1/fyp_CS_2025_WYH.pdf http://eprints.utar.edu.my/7246/ |
| url_provider | http://eprints.utar.edu.my |