# Configurable Intelligent Design Based on Hierarchical Imitation Models
## 1 Actual AI: Overview of Sources
### Complexity  
The paper [53] proposes a hierarchical system framework that uses iHLBA for job scheduling in a grid environment. iHLBA assigns the most suitable resource for each specific job and compares load clusters to the adaptive balance threshold to balance the load system. Local and global update rules are applied to obtain the updated status of resources and to define a balanced threshold, thus making it possible to assign the next job to the most suitable resource. A local update rule is responsible for updating the status of cluster and resource that was assigned to the job. The job scheduler then uses the new status to assign the next action. A global update rule updates the status of each cluster and resource in the grid system, after the resources complete their jobs. This provides the job scheduler with the most updated information on all clusters and resources, thus making it possible to assign the next job to the most suitable resource. Experimental results show that iHLBA is capable of balancing the total system load and improving performance by choosing the best resource for each specific job based on the updated state of the system. Service-oriented computing has created a new method of service delivery based on pay-as-you-go models in which users consume services based on their Quality of Service (QoS) requirements. In these pay-as-you-go models, users pay for services based on usage and compliance with QoS limits; processing time and cost are two common QoS requirements. Therefore, to create effective planning maps, it is necessary to take into account the prices of services when optimizing performance indicators. The work [54] proposed a heterogeneous constrained budget scheduling (HBCS) algorithm that guarantees execution cost within a budget specified by the user, and minimizes the execution time of the user application. Their results show that this algorithm provides faster execution, guaranteed application cost, and lower time complexity compared to other existing algorithms, subject to a limited budget. The improvements are especially important for more heterogeneous systems, where a 30% reduction in execution time was achieved without an increase in budget. Effective application planning is critical to achieving high performance in heterogeneous computing environments. The application scheduling problem is NP-complete in the general case and also in some limited cases. This important issue has been extensively studied, and the various algorithms proposed in the literature are mainly intended for systems with homogeneous processors. Although several algorithms for heterogeneous processors are described in the literature, they usually require more planning and do not offer reductions in planning costs. The article [55] presents two new scheduling algorithms, heterogeneous early-finish-time (HEFT) and heterogeneous early-finish (HEF) for a limited number of heterogeneous processors to achieve high performance and fast scheduling. The HEFT algorithm selects the task with the highest increasing rank value at each step and assigns the selected task to the processor that minimizes the earliest completion time using an insertion-based approach. The CPOP algorithm uses the sum of ascending and descending rank values to determine task priorities. In the processor selection phase, critical tasks are assigned to a processor, which minimizes the overall execution time for critical tasks. To provide a reliable and unbiased comparison with similar work, a parametric graph generator was developed to generate weighted directed acyclic graphs with different characteristics. The distribution estimation algorithm (EDA) is a well-known stochastic optimization method. The average temporal complexity is an important criterion for measuring the performance of stochastic algorithms. Various types of EDA have been proposed in recent years, but relevant theoretical studies on the temporal complexity of these algorithms are relatively rare.