Data-aware distributed batch scheduling

As the data requirements of scientific distributed applications increase, the access to remote data becomes the main performance bottleneck for these applications. Traditional distributed computing systems closely couple data placement and computation, and consider data placement as a side effect of computation. Data placement is either embedded in the computation and causes the computation to delay, or performed as simple scripts which do not have the privileges of a job. The insufficiency of the traditional systems and existing CPU-oriented schedulers in dealing with the complex data handling problem has yielded a new emerging era: the data-aware schedulers. In this chapter, we discuss the challenges in this area as well as future trends, with a focus on Stork case study.