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Extracting the explore‐exploit intelligence of Physarum to manage the sustainability of an enterprise network

S. Habib,P. Marimuthu

Published 2024 in Expert Syst. J. Knowl. Eng.

ABSTRACT

In this work, we enhance the sustainability of an enterprise network (EN) by complementing it with an expert system that apprehends the explore‐exploit behavioural intelligence of Physarum to survive against the attractive‐adversarial nutritional environment. EN sustainability is dynamic since it depends on how well EN can react to an adversarial environment. We capture a reverse analogy to characterize EN's workload‐environment with Physarum's nutritive‐environment, where the high volume of workloads at the backbone network corresponds to a poor‐nutrient environment. The expert system explores EN to find out how to manage the workloads as Physarum handles its survivability, and exploits the users' workload patterns by grouping the highly communicating users together to redesign the network structure as Physarum's intelligence to exploit energy from rich‐ and poor‐nutrient food sources through redesigned tubular structures. We define two factors, such as nutrient‐intensity and chemo‐attractant to aid the redesign process. EN evolves through a set of redesigned clusters with an objective function to maximize its sustainability for a given set of explored workloads by minimizing the workloads through the backbone. EN evolution terminates when there is no change in the backbone utilization, resembling the organism's stay in a dormant state until it experiences a favourable environment. Our experimental results on an EN with a higher volume of workloads at the backbone producing 14.26 kWh energy consumption demonstrated that the developed expert system reduced the energy consumption to 11.27 kWh, thus enhanced the sustainability from 21% to 61%.

PUBLICATION RECORD

  • Publication year

    2024

  • Venue

    Expert Syst. J. Knowl. Eng.

  • Publication date

    2024-03-27

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.1111/exsy.13580
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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