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Cloud computing

Energy-Conscious Scheduling Framework for Serverless Edge Computing in IoT

Strategic Insights

— 1 min read

Researchers at Islamic Azad University have developed an energy-conscious scheduling framework for serverless edge computing in IoT (Internet of Things) environments. The framework is designed to optimize resource allocation and reduce energy consumption while maintaining system availability.

Key Takeaways:

  • The proposed framework, called energy-conscious intelligently-driven scheduler, considers residual energy across the network of currently operational nodes to make scheduling decisions.
  • The approach prevents energy waste and reduces energy consumption by an average of 1.54% compared to other methods.
  • Network availability has increased by an average of 6.4% using the proposed framework.
  • The framework has demonstrated uninterrupted performance in terms of fault tolerance across all scenarios.
  • Experimental outcomes show the proposed method's capability in reducing energy usage while enhancing the stability and robustness of serverless environments.
  • The study employed three distinct workload distribution models to assess the effectiveness of the proposed method.
  • The research was conducted by Mohsen Ghorbian, Mostafa Ghobaei-Arani, and Leila Esmaeili, with Ghorbian serving as the primary author.
  • The study's findings suggest that pre-schedulers guided by forecasts of residual energy in operational network nodes can enhance resource efficiency and access latency.
  • The framework's ability to increase system availability and reduce energy waste makes it a promising solution for IoT environments.

Statistics:

  • Average reduction in energy consumption: 1.54%
  • Average increase in network availability: 6.4%
  • Number of workload distribution models employed: 3
  • Timeframe of experimental outcomes: Not specified
  • Publisher of the journal article: SpringerOpen
  • DOI of the journal article: 10.1186/s13677-025-00780-7

Sources:

  • An energy-conscious scheduling framework for serverless edge computing in IoT. Journal of Cloud Computing: Advances, Systems and Applications, 2025,14(1):1-13.
  • https://journalofcloudcomputing.springeropen.com
  • https://doi-org.sdpl.idm.oclc.org/10.1186/s13677-025-00780-7

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