  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.
