Exploring the Throughput-Fairness Tradeoff of Deadline Scheduling in Heterogeneous Computing Environments

Abstract

The scalability and computing power of large-scale computational platforms that harness processing cycles from distributed nodes has made them attractive for hosting compute-intensive time-critical applications. Many of these applications are composed of computational tasks that require specific deadlines to be met for successful completion. In scheduling such tasks, replication becomes necessary due to the heterogeneity and dynamism inherent in these computational platforms. In this paper, we show that combining redundant scheduling with deadline-based scheduling in these systems leads to a fundamental tradeoff between throughput and fairness. We propose a new scheduling algorithm called Limited Resource Earliest Deadline (LRED) that couples redundant scheduling with deadline-driven scheduling in a flexible way by using a simple tunable parameter to exploit this tradeoff. Our evaluation of LRED using trace-driven and synthetic simulations shows that LRED provides a powerful mechanism to achieve desired throughput or fairness under high loads and low timeliness environments, where these tradeoffs are most critical.