Scalable Cloud Media Infrastructure: Periodic Defragmentation Strategies for Virtualized Storage Optimization

As media processing workloads continue to expand in scale and complexity, virtualized storage systems face increasing pressure to deliver efficient and reliable resource allocation. One of the persistent challenges in such environments is storage fragmentation, where dynamic allocation and deallocation result in scattered, unusable free space. This study addresses the underexplored issue of fragmentation in virtualized media storage, focusing on its impact on allocation performance. The objective of the research is to evaluate the effectiveness of periodic defragmentation in improving storage efficiency and allocation success. A custom simulation framework was developed to model typical media workload behaviors, incorporating features such as random VM lifespans, abnormal terminations, and varying allocation sizes. Two scenarios—baseline unmanaged storage and periodic defragmentation at fixed intervals—were compared based on fragmentation index, allocation success rate, and operational overhead. Results revealed that periodic defragmentation led to a 9.1% reduction in fragmentation and a substantial 52.1% improvement in allocation success, albeit with a total of 7,209 block movement operations. These findings demonstrate that even modest reductions in fragmentation can significantly enhance resource utilization. The study highlights the practical value of scheduled defragmentation and suggests that adaptive, workload-aware strategies could further improve efficiency in future implementations.