Efficient multi-tenant virtual machine allocation in cloud data centers
Open accessAbstract
Virtual Machine (VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant's VMs may considerably impact the tenant's Quality of Service (QoS). In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs' network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem (LP-MKP). The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the QoS among tenants, and improves tenants' overall QoS in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.
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The Digital Twin introduces a more agile perspective to the improvement of product functionalities in manufacturing.
“The Digital Twin thus introduces a more agile perspective to the improvement of product functionalities in manufacturing [18].”
From Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020) · cites this paper as [18]
The Introduction and §2.1 Background discuss virtual-machine allocation and cloud data centers, not Digital Twins or manufacturing. Searches for Digital Twin, agile product improvement, and manufacturing found no passage addressing the claim; absence of support is not a contradiction. (LLM, conf=0.78)
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Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models
Minerva, R., Lee, G. M., & Crespi, N. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2998530 · Cited by 812 · see citing works
- Authors
- Li, J., Li, D., Ye, Y., & Lu, X.
- Affiliations
- National University of Defense Technology (CN)
- Venue
- Tsinghua Science and Technology
- Publisher
- Tsinghua University Press
- Published
- 2015-02
- Volume
- 20
- Issue
- 1
- Pages
- 81-89
- ISSN
- 1007-0214 (ISSN-L)
- OpenAlex
- W2049000915
- Cited by
- 45
- References
- 0
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 7 · 2019: 2 · 2018: 5 · 2017: 4 · 2016: 12 · 2015: 1
- FWCI
- 19.78
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Tsinghua Science & Technology (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61222205, 2011CB302600)
- Program for New Century Excellent Talents in University
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- virtual machine placement, multi-tenancy, cloud data centers, network diameter, quality of service, multiple knapsack problem, resource fragmentation
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