A Pareto-based approach for CPU provisioning of scientific workflows on clouds

As of recently, cloud providers have started offering CPU resources that can be selected from a wide range of different CPU frequencies. CPU resources at higher frequencies have a higher price than CPU resources at lower frequencies that are available at a lower price. When executing applications, such as large scientific workflow applications, multiple CPU resources are required. In this case, this new pricing scheme allows users to choose from a large number of possible CPU configurations that may include relatively fast and relatively slow CPUs. However, such an option raises the problem of how to select appropriate CPU frequency configurations that strike a good balance between cost and execution time performance. As the search space is large with a wide range of choices that have different trade-offs, the problem becomes how to choose Pareto-efficient solutions with respect to execution time and (monetary) cost to use the (CPU) resources. This paper proposes an algorithm to efficiently explore alternative CPU configurations for a given number of resources and identify Pareto-efficient solutions for cost and execution time trade-offs. The algorithm is evaluated through simulation using three different pricing models to charge for CPU provisioning according to the allocated CPU frequency and four widely used scientific workflow applications.