Performance And Cost Analysis Between Elasticity Strategies Over Pipeline-Structured Applications

With the advances in eScience-related areas and the growing complexity of scientific analysis, more and more scientists are interested in workflow systems. There is a class of scientific workflows that has become a standard to stream processing, called pipeline-structured application. Due to the amount of data these applications need to process nowadays, Cloud Computing has been explored in order to accelerate such processing. However, applying elasticity over stage-dependent applications is not a trivial task since there are some issues that must be taken into consideration, such as the workload proportionality among stages and ensuring the processing flow. There are studies which explore elasticity on pipeline-structured applications but none of them compare or adopt different strategies. In this paper, we present a comparison between two elasticity approaches which consider not only CPU load but also workload processing time information to reorganize resources. We have conducted a number of experiments in order to evaluate the performance gain and cost reduction when applied our strategies. As results, we have reached an average of 72% in performance gain and 73% in cost reduction when comparing non-elastic and elastic executions.