Tractor: traffic-aware and power-efficient virtual machine placement in edge-cloud data centers using artificial bee colony optimization
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Summary: Technology providers heavily exploit the usage of edge‐cloud data centers (ECDCs) to meet user demand while the ECDCs are large energy consumers. Concerning the decrease of the energy expenditure of ECDCs, task placement is one of the most prominent solutions for effective allocation and consolidation of such tasks onto physical machine (PM). Such allocation must also consider additional optimizations beyond power and must include other objectives, including network‐traffic effectiveness. In this study, we present a multi‐objective virtual machine (VM) placement scheme (considering VMs as fog tasks) for ECDCs called TRACTOR, which utilizes an artificial bee colony optimization algorithm for power and network‐aware assignment of VMs onto PMs. The proposed scheme aims to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and PMs. To evaluate the proposed VM placement solution, the Virtual Layer 2 (VL2) and three‐tier network topologies are modeled and integrated into the CloudSim toolkit to justify the effectiveness of the proposed solution in mitigating the network traffic and power consumption of the ECDC. Results indicate that our proposed method is able to reduce power energy consumption by 3.5% while decreasing network traffic and power by 15% and 30%, respectively, without affecting other QoS parameters.
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Emerging technologies such as SDN and NFV enable network reliability and robustness and improve integration with existing IoT environments.
“With emerging technologies, such as software-defined networking (SDN) and Network Function Virtualization (NFV), this enables reliability and robustness of the network and improves integration with existing IoT environments [59].”
From Edge AI: A survey (2023) · cites this paper as [59]
The Future Research Directions section discusses IoT and attributes system reliability to a blockchain-based framework, while section 2.3 discusses IoT–edge–cloud integration through FogBus. Searches for SDN, NFV, and the claimed reliability and integration benefits found no passage connecting those benefits to SDN or NFV. (LLM, conf=0.76)
Experimental — accuracy not yet measuredTo make proactive caching decisions efficiently, a data processing platform inside the network infrastructure must read and combine inputs from multiple sources and arrive at intelligent insights.
“In order to make proactive caching decisions efficiently, a data processing platform inside the network infrastructure must read and combine inputs from multiple sources and arrive at intelligent insights [59].”
From Edge AI: A survey (2023) · cites this paper as [59]
The Introduction and Performance Evaluation passages discuss cloud resources, virtual-machine placement, and network power consumption. Searches for proactive caching and for a network data-processing platform combining multiple inputs found no passage addressing the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe evolving discussion on Edge Computing has identified four distinct paradigms differentiated by intended markets and business models.
“The evolving discussion on Edge Computing has identified four distinct paradigms differentiated on the basis of intended markets and business models [59].”
From Edge AI: A survey (2023) · cites this paper as [59]
The Introduction discusses cloud-computing services, and the Performance Evaluation discusses edge-cloud data-center topologies, but neither identifies four edge-computing paradigms. Searches for the claimed paradigms and their differentiation by intended markets or business models found no supporting passage in our copy; page numbers were not available. (LLM, conf=0.76)
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- Authors
- Nabavi, S. S., Gill, S. S., Xu, M., Masdari, M., & Garraghan, P.
- Affiliations
- Department of Computer Engineering, Urmia Branch Islamic Azad University Urmia Iran
- Queen Mary University of London (GB)
- Chinese Academy of Sciences (CN)
- Islamic Azad University of Urmia (IR)
- Lancaster University (GB)
- Venue
- International Journal of Communication Systems
- Publisher
- Wiley
- Published
- 2021-02-02
- Volume
- 35
- Issue
- 1
- ISSN
- 1074-5351 (ISSN-L)
- 1099-1131
- OpenAlex
- W3129060337
- Cited by
- 35
- References
- 50
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 11 · 2022: 9 · 2021: 5
- FWCI
- 7.01
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.lancs.ac.uk
- Locations
- International Journal of Communication Systems (publishedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/V007092/1)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- virtual machine placement, artificial bee colony algorithm, energy efficiency, network traffic reduction, multi-objective optimization, fog computing, data center energy consumption, network topology modeling
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