QoE-Oriented Dependent Task Scheduling Under Multi-Dimensional QoS Constraints Over Distributed Networks

Task scheduling as an effective strategy can improve application performance on computing resource-limited devices over distributed networks. However, existing evaluation mechanisms for application completion fail to depict the complexity of diverse applications and time-varying networks, which involve dependencies among tasks, computing resource requirements, multi-dimensional quality of service (QoS) constraints, and limited contact duration among devices. Furthermore, traditional QoS-oriented task scheduling strategies struggle to meet the performance requirements without considering differences in satisfaction and acceptance of the application, leading to 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 over the time-varying 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 over the time-varying distributed networks, which is proven Np-hard. Moreover, a heuristic Hierarchical Multi-queue Task Scheduling (HMTS) algorithm 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.

I. Introduction

With the unprecedented evolution of communication and computing technologies, more connected devices, smart applications, and massive amounts of data have surged explosively. According to IDC’s predictions, there will be 55 billion connected Internet of Things (IoT) devices worldwide by 2025, which support a wide variety of computing-intensive applications, e.g., autonomous driving, virtual reality, and medical diagnosis. Most of these applications are intelligent and diversified, comprising dependent tasks [1]. For instance, a medical diagnosis application used in telemedicine involves a series of dependent tasks, including data acquisition, preprocessing, image analysis, diagnosis, and report generation. Each of these dependent tasks relies on the output of the preceding tasks and should be processed according to the logical order. However, these increasing complexity and service requirements of applications bring challenges for the lightweight IoT devices, originally designed for better portability and usability. To address this challenge, task scheduling as an effective strategy allows computational tasks to be scheduled across different devices for utilizing distributed computing resources, thereby reducing execution latency and providing satisfactory performance of applications through cooperative processing.