CNN-Brown Bear Optimization Based Energy-Aware Load Balancing in Hybrid Edge-Cloud Architectures for Renewable Energy Networks

Amit Akkewar, Dr. Anurag S. D. Rai

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ABSTRACT

Adaptive and energy-efficient computing infrastructures that can manage diverse workloads are essential for the integration of renewable energy into smart grids. Predictive accuracy and global optimization in dynamic energy environments are limited by the use of heuristic scheduling techniques or lightweight artificial intelligence in existing load balancing frameworks. In order to improve load balancing in hybrid edge-cloud renewable energy networks, this paper suggests a novel framework that combines Convolutional Neural Networks (CNNs) with Brown Bear Optimization (BBO). In order to produce precise short-term predictions of energy availability and task demands, the CNN module extracts deep spatiotemporal features from solar irradiance, weather data, and workload traces. A BBO-driven scheduler uses these forecasts to minimize a multi-objective cost function that balances latency, energy consumption, and task completion reliability. Priority-aware scheduling and intelligent task migration between edge and cloud resources are ensured by the dynamic classification of tasks into three categories: Critical Real-Time, Latency-Sensitive, and Delay-Non-Critical. In comparison to LSTM-based and heuristic approaches, simulation studies using real-world solar and grid workload datasets show that the suggested CNN-BBO framework improves task completion rates to 98.6%, maintains low latency, and reduces average energy consumption by over 35%. The outcomes demonstrate that CNN-BBO integration offers next-generation smart energy infrastructures a scalable, resilient, and sustainable solution.

Keywords: Convolutional Neural Networks (CNN), Brown Bear Optimization (BBO), Energy-Aware Load Balancing, Edge-Cloud Computing, Renewable Energy Networks, Smart Grids, Task Scheduling, AI-Driven Optimization

How to cite this article: Akkewar A, Rai ASD. CNN-Brown Bear Optimization Based Energy-Aware Load Balancing in Hybrid Edge-Cloud Architectures for Renewable Energy Networks. Int J Drug Deliv Technol. 2026;16(16s): 29-37. DOI: 10.25258/ijddt.16.16s.5

Source of support: Nil.

Conflict of interest: None