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@article{183421,
        author = {Sanchee Kaushik},
        title = {Green Orchestration: Minimizing Compute and Carbon Footprint in Cloud-Based Data Pipelines},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2331-2338},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183421},
        abstract = {Cloud computing is the backbone of modern digital infrastructure which carries a hidden environmental cost: high compute and carbon footprints. As data pipelines scale globally, orchestrating them sustainably becomes critical. This review explores emerging strategies for green orchestration in cloud-based data pipelines. It introduces the Carbon-Intelligent Orchestration Framework (CIOF) and evaluates tools and approaches that reduce emissions without compromising performance.
We synthesize current research on carbon-aware scheduling, green autoscaling, and AI-augmented orchestration. Experimental case studies and performance benchmarks are reviewed to assess real-world impact. Green orchestration methods including ML-based autoscaling and carbon-intelligent Kubernetes schedulers reduce emissions by 20-33% while improving compute utilization and reducing costs. The CIOF model provides a structured framework for implementing these strategies.Green orchestration represents a scalable and pragmatic pathway toward digital sustainability. By integrating carbon metrics into orchestration logic, organizations can align cloud efficiency with climate goals.},
        keywords = {Green Orchestration, Cloud Sustainability, Carbon-Aware Scheduling, Data Pipelines, Carbon Footprint, Cloud Computing, Carbon-Intelligent Framework, Energy-Aware Autoscaling, Sustainable DevOps, Green AI Infrastructure},
        month = {August},
        }