Energy-Efficient Resource Management Techniques for Big Data Workloads in Cloud Data Centers

Abstract

This paper addresses the critical challenge of managing energy consumption in cloud data centers running big data workloads. As organizations continue to migrate large-scale applications to the cloud, the demand for efficient resource provisioning, scheduling, and scaling becomes essential for both cost and sustainability reasons. Energy expenditures in data centers can be substantial, and inefficiencies often arise from underutilized resources, ineffective workload placement, and suboptimal scheduling algorithms. In this work, a comprehensive framework is proposed to reduce power consumption without degrading application performance or violating service-level requirements. The discussion encompasses novel mathematical models that capture the intricacies of workload characteristics, computational capacity, network overhead, and cooling demands. Advanced scheduling methods, including heuristic-based and optimization-driven techniques, are explored with the aim of balancing trade-offs between power savings and throughput. Furthermore, simulations and real-world tests demonstrate the feasibility of these approaches and highlight critical factors that influence practical performance, including workload heterogeneity, network constraints, and consolidation strategies. Finally, the paper addresses potential areas where the proposed model encounters limitations, emphasizing the impact of scale, dynamic workload fluctuations, and hardware diversity on optimization outcomes. Through its combination of rigorous theoretical modeling and empirical validation, this research offers valuable insights into designing more energy-aware cloud infrastructures.