Functional Partitioning to Optimize End-to-End Performance on Many-core Architectures
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Deadline and energy constrained dynamic resource allocation in a heterogeneous computing environment
The Journal of Supercomputing
Reliable workflow scheduling with less resource redundancy
Parallel Computing
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This paper addresses the problem of scheduling dynamicallymulti-user and independent jobs on clusters, both homogeneous and heterogeneous. The dynamic behaviormeans that the scheduler is able to adapt the schedulingwhen new jobs are submitted and also when processorsavailability changes. The scheduler has two main featurescomparing to other solutions: it considers a job as beingdescribed by a direct acyclic graph (DAG) and it is ableto schedule parallel tasks, when appropriate, instead of the common dynamic mapping approach that assigns an entirejob to a processor or a fixed set of processors. The scheduling method is divided in a scheduling strategy and a scheduler algorithm, so that other scheduling algorithms can be incorporated. In this paper two static DAG schedulers for heterogeneous machines are considered. The results show the behavior of the scheduling method for the short completion time of a batch of jobs. These results show better performance when compared to the common schedulers strategies that fix the number of processors per job or assign one processor per job.