An Empirical Analysis of Scheduling Techniques for Real-Time Cloud-Based Data Processing

In this paper, the authors explore the challenges and needs of current cloud infrastructures, to better support cloud-based data-intensive applications that are not only latency-sensitive but also require strong timing guarantees. These applications have strict deadlines and deadline misses are undesirable. To highlight the challenges in this paper, they provide a case study of the online scheduling of MapReduce jobs executed by Hadoop. Their evaluations on Amazon EC2 show that the existing Hadoop scheduler is ill-equipped to handle jobs with deadlines.