Performance Optimization of Virtual Machine Allocation in Distributed Cloud Computing Environments
Keywords:
Virtual Machine Allocation, Distributed Cloud Computing, Performance Optimization, Resource Management, Load Balancing, Virtualization, Cloud Scheduling, Energy Efficiency, Resource Utilization, Dynamic AllocationAbstract
The rapid development of cloud computing has increased the demand for efficient resource management and performance optimization in distributed computing environments. Virtual machine (VM) allocation plays a significant role in determining the performance, scalability, reliability, and operational cost of cloud infrastructure. Inefficient VM allocation can result in resource underutilization, excessive energy consumption, workload imbalance, increased processing time, and service-level agreement violations. This study examines performance optimization techniques for virtual machine allocation in distributed cloud computing environments, emphasizing resource utilization, workload distribution, energy efficiency, processing performance, and allocation costs. It investigates the effectiveness of traditional allocation strategies, including first-fit, best-fit, round-robin, and dynamic resource allocation, alongside intelligent scheduling and workload prediction approaches. A conceptual research methodology is developed using performance indicators such as CPU utilization, memory utilization, task completion time, response time, throughput, migration overhead, and energy consumption. The study discusses how adaptive allocation, workload-aware scheduling, resource consolidation, and virtual machine migration can improve infrastructure efficiency. It also examines the importance of balancing computational demands across geographically distributed cloud servers while maintaining service quality. The proposed optimization framework provides a foundation for developing scalable, cost-effective, and energy-efficient VM allocation strategies for modern distributed cloud computing environments.
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