A Sustainable Software Engineering Framework for Energy-Aware Intelligent Systems Using Adaptive Optimization and Real Time Analytics - Dzeze Zakaria Hamzah; Atiek Nurindriani; Robiatul Adawiyah (Asosiasi Riset Teknik Elektro dan Infomatika Indonesia, 2024)
The increasing complexity of modern software systems and the growing demand for real-time data processing have significantly contributed to higher energy consumption in computing infrastructures. This challenge is particularly evident in systems that rely on continuous monitoring, analytics, and adaptive decision-making. Addressing energy efficiency without compromising system performance has therefore become a critical concern in sustainable software engineering. This study proposes an energy-aware software approach that integrates real-time analytics with adaptive feedback mechanisms to optimize energy consumption while maintaining operational performance. The research adopts a design science oriented methodology, encompassing system design, implementation, and experimental evaluation. The proposed system architecture consists of real-time data acquisition, intelligent analytics, and an adaptive control layer based on the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) feedback loop. Experimental evaluations were conducted under dynamic workload scenarios to compare the proposed adaptive system with a baseline non-adaptive system. Key performance indicators included energy consumption, response time, throughput, and adaptation latency. The results demonstrate that the proposed system achieves a substantial reduction in energy consumption while maintaining, and in some cases improving, system performance metrics. The adaptive feedback mechanism enables the system to respond effectively to workload fluctuations, reducing unnecessary energy usage during low-demand periods and ensuring stable performance during peak loads. These findings provide empirical evidence that real-time analytics and adaptive control can effectively support energy-efficient and sustainable software systems. This research contributes to the field of energy-aware software engineering by demonstrating that intelligent real-time adaptation is a viable strategy for achieving sustainability objectives in dynamic and performance-critical environments.