Volume no :
9 |
Issue no :
1
Article Type :
Scholarly Article
Author :
Dr.P.Meenalochini
Published Date :
May, 2025
Publisher :
Journal of Artificial Intelligence and Cyber Security (JAICS)
Page No: 1 - 11
Abstract : As the volume of data stored in cloud environments continues to grow exponentially, efficient data management becomes critical to ensuring scalability, cost-effectiveness, and performance. One of the most effective strategies to optimize cloud storage is data deduplication—a technique that eliminates redundant copies of data to reduce storage space and improve input/output (I/O) performance. This paper explores advanced deduplication techniques and their role in enhancing cloud storage performance. Traditional deduplication methods, such as fixed-size and variable-size chunking, have provided substantial space savings but face limitations in handling complex, large-scale cloud workloads. These methods often result in high computational overhead, latency, and limited adaptability to evolving data patterns. To address these challenges, we investigate next-generation deduplication techniques that leverage machine learning, content-defined chunking (CDC), and hybrid approaches combining inline and post-process deduplication. Our research emphasizes the importance of intelligent data chunking and indexing mechanisms that adapt to varying data types and usage patterns. By integrating similarity detection algorithms and predictive models, advanced deduplication can preemptively identify redundant data with greater accuracy, thereby reducing processing time and resource consumption. Furthermore, distributed deduplication frameworks are examined, highlighting their potential to scale across cloud infrastructures while maintaining deduplication efficiency and fault tolerance. We also analyze the performance implications of deduplication on cloud systems, including its impact on storage latency, throughput, and network bandwidth utilization. Experimental results from simulations and real-world deployments demonstrate that advanced deduplication can achieve up to 70–90% storage savings while improving read/write performance by minimizing data transfer and disk I/O. Additionally, we discuss the trade-offs between storage optimization and computational cost, as well as strategies to mitigate deduplication-induced latency. Security and privacy concerns are addressed by evaluating the risks of data leakage through fingerprinting and proposing encryption-compatible deduplication schemes. The paper concludes with recommendations for implementing scalable, secure, and efficient deduplication systems in cloud environments, alongside future research directions in deduplication-aware storage architectures and edge-cloud collaboration. In summary, advanced deduplication techniques offer a promising path toward more efficient and performant cloud storage solutions. By intelligently eliminating redundancy while preserving system responsiveness and data integrity, these methods support the growing demand for reliable, high-performance cloud infrastructure in diverse application domains.
Keyword Cloud storage, data deduplication, storage optimization, performance enhancement, data redundancy elimination, cloud computing, efficient storage management, backup optimization, storage cost reduction, duplicate data detection, scalable storage solutions, data compression, storage efficiency, cloud data management, advanced deduplication algorithms.
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