Improving adaptivity and fairness of processing real-time tasks with QoS requirements on clusters through dynamic scheduling

In this paper, we consider the problem of scheduling a set of independent real-time tasks with QoS requirements on a cluster, in which the adaptivity and fairness are the two important performance metrics. Thereby, we propose a novel scheduling strategy named AFSS that can guarantee: 1) excellent adaptivity, i.e., more real-time tasks can be accepted when the system is in heavy workload, and real-time tasks have high QoS levels when the system is lightly loaded; 2) fairness, i.e., real-time tasks have fair QoS levels to hold smooth processing quality. The GS and LA algorithms in AFSS are discussed and analyzed. Further, several theorems are given to prove the effectiveness of AFSS.

► A novel scheduling strategy named AFSS was proposed.
► AFSS can guarantee excellent adaptivity and fairness.
► The GS and LA algorithms in AFSS are discussed and analyzed.
► Several theorems are given to prove the effectiveness of AFSS.