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Frugal Self-Optimization Mechanisms for Edge-Cloud Continuum.

Zofia Wrona1, Katarzyna Wasielewska-Michniewska2, Maria Ganzha1

  • 1Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland.

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Summary
This summary is machine-generated.

This study introduces a lightweight module for self-optimization in the Edge-Cloud Continuum (ECC). It integrates anomaly detection and adaptive sampling to enhance system autonomy and reliability for resource-constrained IoT devices.

Keywords:
adaptationanomaly detectioncloud-edge continuumfrugalitysamplingself-*

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • The Edge-Cloud Continuum (ECC) faces increasing complexity due to IoT expansion and data-intensive applications.
  • Current systems require innovative methods for automated and efficient management, focusing on self-* capabilities.
  • Anomaly detection and adaptive sampling are crucial for optimizing data transmission and system reliability.

Purpose of the Study:

  • To integrate anomaly detection and adaptive sampling into a single, lightweight, and extendable self-optimization module.
  • To design a module suitable for resource-constrained devices in distributed ECC environments.
  • To enhance system autonomy, operational proactiveness, and resilience of critical processes.

Main Methods:

  • Implementation of density-based anomaly detection in real-time resource utilization data streams.
  • Integration of a dynamic adaptive sampling technique using Probabilistic Exponential Weighted Moving Average.
  • Validation using synthetic and real-world datasets, including target infrastructure samples.

Main Results:

  • Demonstrated effectiveness of implemented techniques in diverse, near real-life scenarios.
  • Performance comparison with state-of-the-art algorithms, highlighting advantages and limitations.
  • Showcased the module's capability for resource-aware autonomic optimization.

Conclusions:

  • The proposed module offers a novel perspective on resource-aware autonomic optimization for next-generation ECC.
  • The module is well-suited for distributed systems with highly resource-constrained devices.
  • Emphasis on frugality and real-time operation enhances the resilience of critical processes.