Abstract
Now we are shifting toward to a data driven economy, in which the ability to efficiently analyze huge amount of data in time is the key to successes. Many systems for big data processing have been developed and Storm is one of them, whose target is stream data processing. By default Storm only provides a very simple round robin scheduling policy to assign tasks. The default scheduler can provides nice performance for homogeneous platforms, but does not work well for heterogeneous computing environments. In this thesis, we propose and implement a new Storm scheduling algorithm, named G-Storm, such that Storm can evaluate GPU capacity for scheduling and more effectively make use of GPU to speed up the overall performance. The experimental results show that G-Storm can achieve 1.65x to 2.04x performance acceleration on lightly weight and heavily loading of topology, compared to Storm with default scheduler.