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  • TRACTOR-Final (2 Feb)

    Rights statement: This is the peer reviewed version of the following article: TRACTOR: Traffic‐aware and power‐efficient virtual machine placement in edge‐cloud data centers using artificial bee colony optimization. doi: 10.1002/dac.4747 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/dac.4747/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

    Accepted author manuscript, 1.17 MB, PDF document

    Embargo ends: 3/02/22

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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TRACTOR: Traffic‐aware and power‐efficient virtual machine placement in edge‐cloud data centers using artificial bee colony optimization

Research output: Contribution to journalJournal articlepeer-review

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  • Sayyid Shahab Nabavi
  • Sukhpal Singh Gill
  • Minxian Xu
  • Mohammad Masdari
  • Peter Garraghan
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Article numbere4747
<mark>Journal publication date</mark>2/02/2021
<mark>Journal</mark>International Journal of Communication Systems
Number of pages21
Publication StatusAccepted/In press
<mark>Original language</mark>English

Abstract

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.

Bibliographic note

This is the peer reviewed version of the following article: TRACTOR: Traffic‐aware and power‐efficient virtual machine placement in edge‐cloud data centers using artificial bee colony optimization. doi: 10.1002/dac.4747 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/dac.4747/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.