Event-Driven Data Engineering with Adaptive Stream Processing for Real-Time Cloud Workload Orchestration
Keywords:
Energy Efficiency, Task Scheduling, Virtualization, Cloud Computing, Resource Management, Virtual Machines, Power Optimization, Virtual Machine Consolidation, Load Balancing, Green ComputingAbstract
Energy-efficient task scheduling and resource management have become essential research areas in virtualized cloud infrastructure due to the increasing demand for computational resources, growing data centre electricity consumption, and the need for sustainable computing environments. Virtualization enables multiple virtual machines (VMs) to operate on shared physical servers, improving resource utilization and reducing infrastructure costs. However, inefficient task allocation, excessive server utilization, idle resources, and unnecessary virtual machine migrations can increase energy consumption and operational expenses. This study examines energy-efficient task scheduling and resource management techniques for virtualized cloud environments, focusing on dynamic resource allocation, virtual machine consolidation, workload balancing, power-aware scheduling, and resource optimization. The research methodology considers a conceptual simulation-based framework for evaluating scheduling performance through energy consumption, resource utilization, execution time, throughput, and quality of service. The discussion emphasizes the relationship between computational performance and energy efficiency and examines how intelligent scheduling strategies can improve infrastructure sustainability. The study establishes that combining workload prediction, dynamic virtual machine placement, and power-aware resource management provides an effective foundation for reducing energy consumption while maintaining service-level requirements. The proposed framework also identifies opportunities for developing adaptive and environmentally sustainable cloud computing systems
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