Algorithms for Scheduling Deadline-Sensitive Malleable Tasks

Due to the ubiquitous batch data processing in cloud computing, the fundamental model of scheduling malleable batch tasks and its extensions have received significant attention recently. In this model, a set of n tasks is to be scheduled on C identical machines and each task is specified by a value, a workload, a deadline and a parallelism bound. Within the parallelism bound, the number of the machines allocated to a task can vary over time and its workload will not change accordingly. In this paper, the two core results of this paper are to quantitatively characterize a sufficient and necessary condition such that a set of malleable batch tasks with deadlines can be feasibly scheduled on C machines, and to propose a polynomial time algorithm to produce such a feasible schedule. The core results provide a conceptual tool and an optimal scheduling algorithm to enable proposing new analysis and design of algorithms or improving existing algorithms for extensive scheduling objectives.