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

2025-01-20

AI Summary

To address insufficient infrastructure–analytics co-elasticity in dynamic IoT data streams within the computing continuum, this paper proposes the first unified elasticity framework jointly modeling multidimensional resources (CPU, memory, bandwidth) and data semantic metrics (coverage, sampling rate, freshness). We design a closed-loop control architecture centered on an elasticity orchestrator, integrating real-time sensing, multi-objective optimization-based scheduling, and lightweight policy inference to enable cross-layer (edge-to-cloud) joint decision-making. Experimental evaluation demonstrates that the framework achieves over 95% SLA compliance under sudden workload surges, reduces end-to-end latency by 42%, and significantly improves both responsiveness and resource utilization efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• IoT Applications
• Dynamic Resource Management
• Cloud Technology Limitations

Innovation

• Dynamic Resource Adjustment
• IoT Environment
• Enhanced System Efficiency