Time series forecasting is a fundamental task in many domains, such as finance, energy, and intelligent systems. It is increasingly important in modern computing environments, including cloud computing and distributed resource management. However, real-world time series often exhibit complex temporal dependencies, high volatility, and multi-scale nonlinear patterns, making accurate forecasting challenging. To address these issues, this work proposes SVBTformer, a novel and effective forecasting model that enhances the Transformer-based Informer architecture with structured temporal learning modules. Specifically, SVBTformer integrates Savitzky–Golay (SG) filtering for noise reduction and signal smoothing, followed by Variational Mode Decomposition (VMD) to extract multi-resolution temporal components. Then, an improved Informer network called BTformer is employed to enhance the modeling capability for time series and strengthen the extraction of temporal dependencies. This work extensively experiments on publicly available benchmarks spanning multiple domains, including the ETT dataset for electric power demand, foreign exchange rates, and meteorological measurements. The results demonstrate that SVBTformer consistently outperforms state-of-the-art models, such as Informer and Autoformer, across most evaluation metrics, delivering superior accuracy and robustness. These gains underscore SVBTformer’s strong generalization capability and suitability for deployment in various real-world time series applications.

Distributed systems form the critical infrastructure supporting high-performance computing and complex simulations, with effectiveness heavily dependent on load balancing strategies. Within these systems, distributed simulation tasks present unique challenges through strict temporal dependencies and sequential constraints that traditional methods struggle to address. This paper proposes CGWSA—a Color-Graph Grey Wolf-Simulated Annealing hybrid algorithm—that fundamentally advances load balancing for distributed simulation workloads. Our methodology introduces a comprehensive dual-layer framework that considers both node heterogeneity and intricate task dependencies: 1) Color-Graph preprocessing that categorizes tasks by resource dominance patterns, enabling efficient parallelization while preserving execution priorities; and 2) A bio-inspired optimization engine that combines Grey Wolf Optimizer’s hierarchical search capabilities with Simulated Annealing’s probabilistic acceptance mechanism to prevent local optima trapping. Experimental results on simulated computing environments demonstrate CGWSA’s superiority with the lowest load balance degree of 1.678 and optimal makespan of 9.31 seconds—10.6% faster than the second-best approach. The algorithm’s dependency-aware scheduling architecture establishes new performance standards for time-sensitive simulation computing while maintaining applicability across diverse distributed environments including cloud computing and smart manufacturing systems.

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios.

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization.