Requirement-aware strategies for scheduling real-time divisible loads on clusters

• In this paper we consider a real-time scheduling problem for handling heterogeneous divisible loads on cluster systems.
• We consider divisible loads associated with deadlines to enhance quality-of-service (QoS) and provide performance guarantees.
• We propose scheduling algorithms referred to as Requirements-Aware Real-Time Scheduling (RARTS) algorithms.
• We perform rigorous performance evaluation studies to quantify the performance of our strategies on a variety of scenarios.

This paper investigates the real-time scheduling problem for handling heterogeneous divisible loads on cluster systems. Divisible load applications occur in many fields of science and engineering. Such applications can be easily parallelized in a master–worker fashion, but pose several scheduling challenges. We consider divisible loads associated with deadlines to enhance quality-of-service (QoS) and provide performance guarantees in distributed computing environments. In addition, since the divisible loads to be performed may widely vary in terms of their required hardware and software, we capture the loads’ various processing requirements in our load distribution strategies, a unique feature that is applicable for running proprietary applications only on certain eligible processing nodes. Thus in our problem formulation each load can only be processed by certain processors as both the loads and processors are heterogeneous. We propose scheduling algorithms referred to as Requirements-Aware Real-Time Scheduling (RARTS) algorithms, which consist of a novel scheduling policy, referred to as Minimum Slack Capacity First (MSCF), and two multi-round load distribution strategies, referred to as All Eligible Processors (AEP) and Least Capability First (LCF). We perform rigorous performance evaluation studies to quantify the performance of our strategies on a variety of scenarios.