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<p>In cloud data warehousing, performance optimization enhances speed and efficiency through efficient data ingestion, query optimization, and effective load balancing. Blockchain secures storage with traceability, but existing methods often neglect security, causing vulnerabilities. So, this paper proposes a performance optimization with enhanced data security system using AC-AKDES. Firstly, data is ingested into the cloud, and attributes are extracted and stored on the blockchain. Next, data is structured using the Hadoop, and hashed using TSSHA-256; next, the hashed data is also stored on the blockchain. Once structured, DB-HL-DCAN partitions the data into smaller, manageable pieces. AC-AKDES is then used for data security. The secured data is load balanced using DB-HL-DCAN. Next, workload and VM features are extracted to select optimal VMs. VM authentication is ensured through Quantum Digital Chaotic Signature Algorithm (QDCSA)-based signature verification. If a VM is authenticated, resources are allocated; if not, the unauthenticated VM is updated on the blockchain. For resource allocation Crowding Factor Replaced Walrus Optimizer (CFRWO) is used. Finally, allocated resources are indexed using B-Tree Indexing (BTI) and stored on the blockchain for secure query processing. Thus, performance of the cloud data warehouse is optimized, and data security is achieved with a security level of 668 bits.The proposed system achieved a 48% reduction in query execution time, 98.65% resource utilization, and a 98.35% task success rate, while attaining a security level of 668 bits with reduced encryption and decryption time. These results confirm that the AC-AKDES and blockchain-enabled framework significantly outperforms existing methods in both performance and security.</p>

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Performance optimization in cloud data warehouse based on blockchain and data security using AC-AKDES approach

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Rahul Jadon,
  • Venkata Surya Teja Gollapalli,
  • Kannan Srinivasan,
  • Rajababu Budda,
  • Guman Singh Chauhan,
  • Joseph Bamidele Awotunde

摘要

In cloud data warehousing, performance optimization enhances speed and efficiency through efficient data ingestion, query optimization, and effective load balancing. Blockchain secures storage with traceability, but existing methods often neglect security, causing vulnerabilities. So, this paper proposes a performance optimization with enhanced data security system using AC-AKDES. Firstly, data is ingested into the cloud, and attributes are extracted and stored on the blockchain. Next, data is structured using the Hadoop, and hashed using TSSHA-256; next, the hashed data is also stored on the blockchain. Once structured, DB-HL-DCAN partitions the data into smaller, manageable pieces. AC-AKDES is then used for data security. The secured data is load balanced using DB-HL-DCAN. Next, workload and VM features are extracted to select optimal VMs. VM authentication is ensured through Quantum Digital Chaotic Signature Algorithm (QDCSA)-based signature verification. If a VM is authenticated, resources are allocated; if not, the unauthenticated VM is updated on the blockchain. For resource allocation Crowding Factor Replaced Walrus Optimizer (CFRWO) is used. Finally, allocated resources are indexed using B-Tree Indexing (BTI) and stored on the blockchain for secure query processing. Thus, performance of the cloud data warehouse is optimized, and data security is achieved with a security level of 668 bits.The proposed system achieved a 48% reduction in query execution time, 98.65% resource utilization, and a 98.35% task success rate, while attaining a security level of 668 bits with reduced encryption and decryption time. These results confirm that the AC-AKDES and blockchain-enabled framework significantly outperforms existing methods in both performance and security.