IEEE Transactions on Software Engineering
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Minimizing Energy Consumption for Precedence-Constrained Applications Using Dynamic Voltage Scaling
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Optimal Scheduling Strategies in a Multiprocessor System
IEEE Transactions on Computers
International Journal of High Performance Computing Applications
A task duplication based bottom-up scheduling algorithm for heterogeneous environments
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems
Journal of Parallel and Distributed Computing
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In this paper, we address the resource minimization problem for DAG-based real-time applications using computer clouds to: (1) guarantee the satisfaction of a real-time application's end-to-end deadline, (2) ensure the number of computers allocated to the application is minimized, and (3) under allocated resources, minimize the application's make span. We first give lower and upper bounds for resources needed to guarantee the satisfaction of a real-time application's deadline. Based on the bounds, we develop a heuristic algorithm called minimal slack time and minimal distance (MSMD) algorithm that finds the minimum number of computers needed to guarantee the application's deadline and schedules tasks on the allocated resources so that the application's make span is minimized. Our experimental results show that the algname algorithm can guarantee applications' end-to-end deadlines with less resources compared with other heuristic scheduling algorithms existed in the literature. In addition, under the minimal allocated resources, the MSMD algorithm can, on average, reduce an application's make span by 10% of its deadline.