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Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things.

Damián Fernández-Cerero1,2, Jorge Yago Fernández-Rodríguez3, Juan A Álvarez-García4

  • 1Department of Computer Languages and Systems, University of Seville, 41012 Seville, Spain. damiancerero@us.es.

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Summary
This summary is machine-generated.

This study introduces a cost-effective edge computing architecture using Single-Board-Computer clusters for Internet of Things (IoT) workloads. The proposed model reduces acquisition costs while maintaining performance for data-intensive applications.

Keywords:
Internet of Thingscloudlet computingdistributed systemsedge computingresource efficiency

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Internet of Things

Background:

  • The proliferation of connected devices in the Internet of Things (IoT) presents challenges for centralized cloud computing due to latency, throughput, and bandwidth constraints.
  • Edge computing emerges as a solution for real-time IoT workloads by processing data closer to the source, mitigating cloud limitations.

Purpose of the Study:

  • To propose and evaluate a cost-efficient edge computing architecture for IoT workloads.
  • To assess the viability of Single-Board-Computer (SBC) clusters as micro data centers for edge computing.
  • To compare the proposed architecture against traditional cloudlet and cloud models.

Main Methods:

  • Development of a hybrid architecture combining SBC clusters and centralized cloud data centers.
  • Extensive empirical analysis of SBC clusters for edge computing suitability.
  • Comparative simulation of the proposed architecture with cloudlet and cloud architectures.

Main Results:

  • The proposed architecture significantly reduces acquisition costs for edge computing infrastructure.
  • Single-Board-Computer clusters demonstrate effectiveness as cost-efficient edge computing micro data centers.
  • The architecture maintains performance levels comparable to traditional models in data-intensive IoT scenarios.

Conclusions:

  • The proposed SBC-based edge computing architecture offers a scalable and cost-effective alternative to fog computing for real-time IoT applications.
  • This model addresses the operational challenges of edge computing for large-scale providers.
  • The findings support the adoption of low-cost SBC clusters for building performant edge computing solutions.