Improving the energy efficiency and performance of data-intensive workflows in virtualized clouds

In recent years, deploying and running data-intensive workflows in cloud platform has become more and more popular in many areas. Unlike computation-intensive applications, a data-intensive workflow typically requires to deal with bulk data transferring between different resource sites, which means some traditional energy-efficiency optimization technologies are difficult to be enforced when running data-intensive workflows. In this paper, we first formulate the power model of a data-intensive workflow, which takes into account power consumption caused by data transferring. Based on this power model, we introduce a novel metric called Shortest Path in terms of Energy Consumption and design an energy-efficient heuristic scheduling algorithm, which is aiming at reducing the extra energy consumption caused by delays of bulk data transferring. Extensive experiments and performance evaluations show that the proposed scheduling algorithm can significantly reduce the overall energy consumption of running data-intensive workflows comparing with several existing algorithms. In addition, the proposed algorithm also exhibits better adaptiveness and robustness when a cloud system is facing intensive and unpredicted workloads.