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<p>The swift proliferation of internet-connected gadgets produces vast quantities of data daily, beyond their constrained processing and storage capacities. Cloud computing has evolved as a viable alternative for the effective management and processing of unexpected data. Nonetheless, resource management and job consolidation in cloud systems continue to pose challenges owing to their complexity. This research presents a hybrid task consolidation technique that integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Binary Chaotic Jaya Optimization (BCJO) strategy to resolve these challenges. BCJO improves the control parameters of ANFIS, while a load-balancing technique guarantees equitable allocation of workloads among cloud Virtual Machines (VMs) for optimal resource use. The algorithm was evaluated using real-world datasets, demonstrating notable enhancements: 26% in service rate, 17% in resource usage, 16% in response time, 5% in energy efficiency, and 23.4% in load balancing. Statistical validation utilizing Holm’s test affirmed its superiority compared to previous methodologies.</p>

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Energy-and-deadline-aware optimal resource management and task consolidation using a hybrid Adaptive Neuro-Fuzzy Inference System with binary Chaotic Jaya optimization for cloud-based systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月6日

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  • Kodanda Dhar Naik,
  • Rasmita Panigrahi,
  • Rashmi Ranjan Sahoo,
  • Kaushik Mishra,
  • Biswa Mohan Sahoo,
  • Arvind Dhaka

摘要

The swift proliferation of internet-connected gadgets produces vast quantities of data daily, beyond their constrained processing and storage capacities. Cloud computing has evolved as a viable alternative for the effective management and processing of unexpected data. Nonetheless, resource management and job consolidation in cloud systems continue to pose challenges owing to their complexity. This research presents a hybrid task consolidation technique that integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Binary Chaotic Jaya Optimization (BCJO) strategy to resolve these challenges. BCJO improves the control parameters of ANFIS, while a load-balancing technique guarantees equitable allocation of workloads among cloud Virtual Machines (VMs) for optimal resource use. The algorithm was evaluated using real-world datasets, demonstrating notable enhancements: 26% in service rate, 17% in resource usage, 16% in response time, 5% in energy efficiency, and 23.4% in load balancing. Statistical validation utilizing Holm’s test affirmed its superiority compared to previous methodologies.