Log Analytics Framework for iFogSim: Energy Consumption Classification of Fog-Edge Systems

Fog computing has emerged as a critical paradigm for supporting latency-sensitive IoT applications by bringing computational resources closer to the network edge. However, the energy efficiency of fog systems remains a major concern due to the heterogeneity and distributed nature of fog nodes. While simulation tools like iFogSim enable performance evaluation, a framework for diagnostic analysis of energy consumption patterns from simulation logs is lacking. In this paper, a novel log analytics framework for iFogSim is proposed, which enables multi-dimensional classification of energy consumption in fog systems. Through large-scale batch simulations of ۲,۰۰۰ distinct configurations of an intelligent surveillance case study, key metrics including computational load, network statistics, and energy consumption are extracted. The framework introduces three novel classification tasks: energy distribution pattern (cloud-centric, edge-dominant, balanced, inefficient), energy efficiency class (high, moderate, low, critical), and workload characterization (compute-intensive, data-intensive, network-intensive, balanced). A Support Vector Machine (SVM) classifier is trained to automatically categorize fog configurations. Experimental results demonstrate the framework's effectiveness in identifying energy-optimal configurations and diagnosing inefficiencies, providing fog architects with a powerful tool for sustainable fog system design. The proposed approach moves beyond traditional energy reporting to offer diagnostic insights that can significantly reduce operational costs and environmental impact.

کلیدواژه ها:

Fog Computing ، Energy Consumption Classification ، iFogSim ، Log Analytics ، Metrics ، Multi-dimensional Support Vector Machine ، Intelligent Surveillance ، Edge Computing ، Sustainable Computing