2015/01/18 by Xiaohu Wu, Wu, Xiaohu, Patrick Loiseau +1
Computer Science · Engineering · #Cloud Computing and Resource Management #Data Structures and Algorithms (cs.DS) #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #Scheduling and Optimization Algorithms #and Cluster Computing (cs.DC)
paper · doi:10.48550/arxiv.1501.04343
openalex publication_date 2015/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Due to the ubiquity of batch data processing in cloud computing, the related problem of scheduling malleable batch tasks and its extensions have received significant attention recently. In this paper, we consider a fundamental model where a set of n tasks is to be processed 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 machines assigned to a task can vary over time without affecting its workload. For this model, we obtain two core results: a sufficient and necessary condition such that a set of tasks can be finished by their deadlines on C machines, and an algorithm to produce such a schedule. These core results provide a conceptual tool and an optimal scheduling algorithm that enable proposing new algorithmic analysis and design and improving existing algorithms under various objectives.