Task scheduling as an effective strategy can improve application performance on computing resource-limited devices over distributed networks. However, existing evaluation mechanisms fail to depict the complexity of diverse applications, which involve dependencies among tasks, computing resource requirements, and multi-dimensional quality of service (QoS) constraints. Furthermore, traditional QoS-oriented task scheduling strategies struggle to meet the performance requirements without considering differences in satisfaction and acceptance of application, leading application failures and resource wastage. To tackle these issues, a quality of experience (QoE) cost model is designed to evaluate application completion, depicting the relationship among application satisfaction, communications, and computing resources in the distributed networks. Specifically, considering the sensitivity and preference of QoS, we model the different dimensional QoS degradation cost functions for dependent tasks, which are then integrated into the QoE cost model. Based on the QoE model, the dependent task scheduling problem is formulated as the minimization of overall QoE cost, aiming to improve the application performance in the distributed networks, which is proven Np-hard. Moreover, a heuristic Hierarchical Multi-queue Task Scheduling Algorithm (HMTSA) is proposed to address the QoE-oriented task scheduling problem among multiple dependent tasks, which utilizes hierarchical multiple queues to determine the optimal task execution order and location according to different dimensional QoS priorities. Finally, extensive experiments demonstrate that the proposed algorithm can significantly improve the satisfaction of applications.

翻译：任务调度作为一种有效策略，可提升分布式网络中计算资源受限设备上的应用性能。然而，现有评估机制难以刻画涉及任务依赖关系、计算资源需求及多维服务质量（QoS）约束的多样应用复杂性。此外，传统面向QoS的任务调度策略未考虑应用满意度和接受度的差异性，难以满足性能需求，导致应用失败与资源浪费。针对这些问题，本文设计了一种体验质量（QoE）成本模型来评估应用完成度，刻画分布式网络中应用满意度、通信与计算资源间的关系。具体而言，考虑QoS的敏感度与偏好性，我们针对依赖任务建模了不同维度的QoS退化成本函数，并将其整合至QoE成本模型中。基于该模型，依赖任务调度问题被形式化为最小化整体QoE成本，旨在提升分布式网络中的应用性能，该问题已被证明为NP难问题。进一步，提出一种启发式分层多队列任务调度算法（HMTSA），用于解决多依赖任务场景下的QoE导向调度问题。该算法利用分层多队列，根据不同维度的QoS优先级确定最优任务执行顺序与位置。最后，大量实验表明，所提算法能显著提升应用满意度。