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

TL;DR

A framework based on multi-dimensional elasticity is introduced, which enables the adaptive management of both infrastructure resources and data analytics requirements and 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.

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.

Figures (4)

• Figure 1: Multidimensional Elasticity Management in CC
• Figure 2: Orchestrator with RL-Based Elasticity Management in the CC.
• Figure 3: Impact of data analytics requirements on CPU usage.
• Figure 4: Orchestrator adapting data analytics requirements to manage CPU usage.