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Extra info for High Performance Computing: First HPCLATAM - CLCAR Latin American Joint Conference, CARLA 2014, Valparaiso, Chile, October 20-22, 2014. Proceedings

Sample text

Checkpointing would also permit a more extensive use of spot instances without compromising the workflow makespan. A second aspect to investigate in the future is a method for determining the proper value of the spotsRatio parameter considering the characteristics of the application and the quality of the available bid price prediction method. Finally it is interesting to study the repercussion of data transfer times and cost during the autoscaling process. These features are crucial for studying new autoscaling techniques designed for big data applications like, for example, MapReduce workflows.

Although, the bid price prediction method (used during the scaling phase) aims to reduce the number of task failures, out-of-bid errors are likely to occur. A. Monge and C. Garc´ıa Garino Algorithm 2. pop() ETC on-demand instance 6: instance ← fastest(task, instancesod ) 7: if instance == null then: no on-demand instances available ETC spot instance 8: instance ← fastest(task, instancess ) 9: schedule(task, instance) submit for execution For such reason, the algorithm addresses the minimization of the negative effects of such failures on the workflow makespan.

In nature, real ants move randomly from one place to another to search for food, and upon finding food and returning to their nest each ant leaves a pheromone that lures other working ants to the same course. When more and more ants choose the same path, the pheromone trail is reinforced and even more ants will further choose it. Over time the shortest paths will be intensified by the pheromone faster since the ants will both reach the food source and travel back to their nest at a faster rate.

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