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Parallel Algorithms for the Multiobjective Virtual Network Function Placement Problem

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Evolutionary Multi-Criterion Optimization (EMO 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12654))

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Abstract

Datacenters are critical to the commercial and social activities of modern society but are also major electricity consumers. To minimize their environmental impact, we must make datacentres more efficient whilst keeping the quality of service high. In this work we consider how a key datacenter component, Virtual Network Functions (VNFs), can be placed in the datacenter to optimise the conflicting objectives of minimizing service latency, packet loss and energy consumption. Multiobjective Evolutionary Algorithms (MOEAs) have been proposed to solve the Virtual Network Function Placement Problem (VNFPP), but state of the art algorithms are too slow to solve the large problems found in industry. Parallel Multiobjective Evolutionary Algorithms (PMOEAs) can reduce execution time by distributing the optimization process over many processes. However, this can hamper the search process as it is inefficient to share information between processes. This paper aims to determine whether PMOEAs can efficiently discover good solutions to the VNFPP. We found that PMOEAs can solve the VNFPP 5–10\(\times \) faster than a sequential MOEA without harming solution quality. Additionally, we found that one parallel algorithm, PPLS/D, found better solutions than other MOEAs and PMOEAs in most test instances. These results demonstrate that PMOEAs can solve the VNFPP faster, and in some instances better, than sequential MOEAs.

This work was supported by EPSRC Industrial CASE and British Telecom (Grant No. 16000177) and UKRI Future Leaders Fellowship (Grant No. MR/S017062/1).

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Exeter, Exeter, UK

    Joseph Billingsley, Ke Li, Wang Miao & Geyong Min

  2. Research and Innovation, British Telecom, London, UK

    Nektarios Georgalas

Authors
  1. Joseph Billingsley
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  2. Ke Li
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  3. Wang Miao
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  4. Geyong Min
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  5. Nektarios Georgalas
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Corresponding author

Correspondence to Joseph Billingsley .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Hisao Ishibuchi

  2. City University of Hong Kong, Kowloon Tong, China

    Qingfu Zhang

  3. Southern University of Science and Technology, Shenzhen, China

    Ran Cheng

  4. University of Exeter, Exeter, UK

    Ke Li

  5. Xi'an Jiaotong University, Xi'an, China

    Hui Li

  6. Xidian University, Xi'an, China

    Handing Wang

  7. East China Normal University, Shanghai, China

    Aimin Zhou

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Billingsley, J., Li, K., Miao, W., Min, G., Georgalas, N. (2021). Parallel Algorithms for the Multiobjective Virtual Network Function Placement Problem. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_56

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_56

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