A Multidimensional Elasticity Framework for Adaptive Data Analytics Management in the Computing Continuum

TL;DR

This paper presents a multi-dimensional elasticity framework for adaptive management of infrastructure and data analytics in the computing continuum, addressing the challenges of dynamic IoT environments.

Contribution

It introduces a novel framework that dynamically adjusts resources and analytics requirements, improving system adaptability and performance in IoT computing environments.

Findings

The framework effectively balances system performance and resource utilization.

Dynamic adjustments improve responsiveness to changing data analytics demands.

Evaluation shows enhanced system efficiency across edge and cloud nodes.

Abstract

The increasing complexity of IoT applications and the continuous growth in data generated by connected devices have led to significant challenges in managing resources and meeting performance requirements in computing continuum architectures. Traditional cloud solutions struggle to handle the dynamic nature of these environments, where both infrastructure demands and data analytics requirements can fluctuate rapidly. As a result, there is a need for more adaptable and intelligent resource management solutions that can respond to these changes in real-time. This paper introduces a framework based on multi-dimensional elasticity, which enables the adaptive management of both infrastructure resources and data analytics requirements. The framework leverages an orchestrator capable of dynamically adjusting architecture resources such as CPU, memory, or bandwidth and modulating data analytics requirements, including coverage, sample, and freshness. The framework has been evaluated, demonstrating the impact of varying data analytics requirements on system performance and the orchestrator's effectiveness in maintaining a balanced and optimized system, ensuring efficient operation across edge and head nodes.