Workflow scheduling in Heterogeneous Distributed Computing Environments (HDCEs) is a NP-hard problem. Although a number of scheduling approaches have been proposed for workflow scheduling in HDCEs, there is still a room and need for improvement. To fill the gaps, this study formulates workflow scheduling problem in HDCEs as a complete, solvable and extensible integer programming mathematical model with precedence and resource constraints that provides a theoretical foundation for developing workflow scheduling strategy. Then, this study develops a novel two-stage multi-population genetic algorithm with heuristics for workflow scheduling. In particular, two-stage multi-population coevolution strategy is employed with designed novel methods for population initialization, genetic operation, individual decoding and improvement. To estimate the validity, extensive experiments are designed and conducted on various scenarios based on real and random workflow applications. The results have shown the practical viability of the proposed algorithm outperforming conventional approaches.

With the enormous increase in energy usage by cloud data centers for handling various workflow applications, the energy-aware cloud workflow scheduling has become a hot issue. However, there is still a need and room for improvement in both the model for estimating workflow energy consumption and the algorithm for energy-aware cloud workflow scheduling. To fill these gaps, a new model for estimating the energy consumption of the cloud workflow execution and a novel Two-Stage Estimation of Distribution Algorithm with heuristics (TSEDA) for energy-aware cloud workflow scheduling are proposed based on the relationships among scheduling scheme, host load and power. In particular, in the proposed TSEDA, a new probability model and its updating mechanism are presented, and a two-stage coevolution strategy with some novel heuristic methods for individual generation, decoding and improvement is designed. Extensive experiments are conducted on workflow applications with various sizes and types, and the results show that the proposed TSEDA outperforms conventional algorithms.