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Scientific workflows are generally computing- and data-intensive with large volume of data generated during their execution. Therefore, some of the data should be saved to avoid the expensive re-execution of tasks in case of exceptions. However, cloud-based data storage services come at some expense. In this paper, we extend the risk evaluation model, which assigns different weights to tasks based...
The robustness of a schedule, with respect to its probability of successful execution, becomes an indispensable requirement in open and dynamic service-oriented environment, such as grids or clouds. We design a fine-grained risk assessment model customized for workflows to precisely compute the cost of failure of a schedule. In comparison with current course-grained model, ours takes the relation...
Robustness and reliability with respect to the successful completion of a schedule are crucial requirements for scheduling in multi-agent systems because agent autonomy makes execution environments dynamic and nondeterministic. We introduce a model to incorporate trust which indicates the probability that an agent will comply with its commitments into scheduling, thus improving the predictability...
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