Sustainable optimization of data processing waste in distributed systems: Addressing energy consumption and electronic waste in data-intensive application

Distributed systems encounter significant challenges related to energy consumption, electronic waste (e-waste), and the handling of redundant data, all of which contribute to higher operational costs and a greater environmental footprint. This study explores how a cohesive framework for sustainable optimization can tackle issues of energy use, e-waste, and resource management in distributed systems. The main goal is to create and implement sustainable computing strategies, such as workload balancing, dynamic voltage and frequency scaling (DVFS), and data deduplication. These methods are designed to enhance power efficiency, minimize e-waste through circular economy practices, and streamline data management to reduce redundancy. The research utilizes a hybrid approach that combines theoretical modeling with simulation-based analysis to assess the effectiveness of these strategies. Simulations were performed to evaluate system performance regarding energy consumption, e-waste production, and overall efficiency under different workload scenarios. Key findings indicate that the proposed framework can lower energy consumption by 25% through DVFS, cut e-waste by 30% through hardware reuse aligned with circular economy principles, and boost system efficiency by 20% via workload balancing and data deduplication. These quantitative results highlight the potential of the proposed strategies to enhance operational efficiency and promote environmental sustainability. This study offers a guide for system designers, policymakers, and industry professionals to integrate sustainable practices into the design of distributed systems. By embracing green computing principles, stakeholders can lower operational costs, enhance resource utilization, and contribute to global sustainability initiatives in distributed computing.