A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center Article Swipe
Fagui Liu
,
Zhenjiang Ma
,
Bin Wang
,
Weiwei Lin
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2961786
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2961786
The energy consumption issue of large-scale data centers is attracting more and more attention. Virtual machine consolidation can significantly reduce energy consumption by migrating virtual machines from one physical machine to another. However, excessive virtual machine consolidation can lead to dangerous Service Level Agreement (SLA) violations. Therefore, how to balance between effective energy consumption and SLA violations avoidance effectively is a paradox to be mediated. The virtual machine consolidation problem is NP-hard. The traditional heuristic algorithm is easy to fall into the local optimal and some meta-heuristic algorithms can help to avoid it. However, the existing meta-heuristic algorithms are with high complexity. Therefore, we propose a lower complexity multi-population ant colony system algorithm with the Extreme Learning Machine (ELM) prediction (ELM_MPACS). The algorithm firstly predicts the host state employing ELM and then the virtual machine on the overloaded host will be migrated to the normal host, while the virtual machine on the underloaded host will be consolidated to another underloaded host with higher utilization. Multiple populations concurrently construct migration plans and local search further optimizes the results obtained by each population to reduce SLA violations. We compare ELM_MPACS with the benchmark, heuristic and meta-heuristic algorithms. The experimental results have shown that compared with these algorithms, our algorithm reduces energy consumption, migration times and SLA violations effectively.
Related Topics
Concepts
Computer science
Virtual machine
Service-level agreement
Algorithm
Population
Extreme learning machine
Cloud computing
Machine learning
Host (biology)
Artificial intelligence
Energy consumption
Distributed computing
Operating system
Engineering
Demography
Artificial neural network
Ecology
Sociology
Electrical engineering
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2961786
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/08939445.pdf
- OA Status
- gold
- Cited By
- 39
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2998399378
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2998399378Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2019.2961786Digital Object Identifier
- Title
-
A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data CenterWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-23Full publication date if available
- Authors
-
Fagui Liu, Zhenjiang Ma, Bin Wang, Weiwei LinList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2961786Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/08939445.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/08939445.pdfDirect OA link when available
- Concepts
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Computer science, Virtual machine, Service-level agreement, Algorithm, Population, Extreme learning machine, Cloud computing, Machine learning, Host (biology), Artificial intelligence, Energy consumption, Distributed computing, Operating system, Engineering, Demography, Artificial neural network, Ecology, Sociology, Electrical engineering, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
39Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 10, 2023: 10, 2022: 3, 2021: 8Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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