SAQA: A Self-Adaptive QoS-Aware Scheduling Algorithm for Real-Time Tasks on Heterogeneous Clusters

Nowadays, providing quality of service (QoS) guarantees for some applications such as signal data processing has become a critical issue. In this paper, we propose a novel self-adaptive QoS-aware scheduling algorithm called SAQA that sufficiently considers the adaptability for real-time tasks with QoS demands on heterogeneous clusters. When the system is in heavy load, the SAQA algorithm can degrade the QoS levels of new tasks or tasks waiting in local queues of nodes to guarantee high schedul ability. The minimum QoS level is acceptable for each task. In contrast, when the system is in light load, SAQA can use slack time to adequately improve the QoS of new tasks. We compare SAQA with SAEDF algorithm by simulations. The experimental results indicate that SAQA has admirable adaptability while providing timing and QoS guarantees.