Artificial intelligence applications are developing rapidly, placing higher demands on the efficient use of computational power. Workflow offloading in wide-area environments can effectively utilize resources to improve task execution efficiency. However, cross-domain computational power scheduling and peak business periods often lead to network congestion and computational node overload, resulting in imbalanced utilization of wide-area computational resources. This makes it difficult to ensure the overall stability of the computational network and the fairness of resource utilization. This paper proposes the workflow scheduling method of computing power network based on load balance of voting mechanism. First, a multi-objective optimization model for workflow task scheduling is constructed. Then, a Voting-mechanism-based Multi-objective Optimization Algorithm is proposed, which combines the Evolutionary Multiobjective Optimization Algorithm based on Unequal Grid Division (IGEA) and the Multi-objective Artificial Rabbit Optimization Algorithm (MOARO). This method adaptively adjusts the weight of each algorithm during the optimization process to optimize the completion time of computationally intensive and data-intensive workflows, as well as the computational network resource utilization. Finally, experiments are conducted on different workflow combination scenarios. The proposed algorithm is compared with the MOARO and IGEA multiobjective optimization algorithms to validate the effectiveness of the method presented in this paper.

This paper proposes a switching and scheduling method for containerized computing environments. The core strength of the method lies in establishing a complete technical framework: standardized image templates and persistent storage ensure data consistency and availability during heterogeneous environment transitions, while the proposed Priority-based Resource Scheduling (PRS) serves as the key to efficient switching and resource allocation. Experimental results demonstrate that PRS achieves significant performance improvements over traditional strategies such as FCFS, SJF, and Random. Specifically, PRS reduces overall task completion time by approximately$5.2\%$, maintains cluster GPU time utilization consistently above$98\%$, and demonstrates superior stability compared to alternative strategies. By leveraging dynamic prediction and priority scheduling to prioritize critical tasks, the proposed method significantly improves overall task completion time and resource utilization in heterogeneous computing environments.

This work proposes a resource-aware adaptive framework for MobileNetV4 that dynamically balances accuracy, latency, and memory under edge device constraints. A Dynamic Universal Inverted Bottleneck (UIB) module architecture is designed with hierarchical decomposition into Perception Block (PB), Semantic Aggregation Block (SAB), and Decision Block (DB). Through dual pruning and reinforcement learning-based variant selection, the framework achieves superior accuracy-efficiency trade-off across diverse hardware. Experiments show up to 30% inference speedup and 67% memory usage compared to the baseline, with$<5 \%$accuracy drop.