Enhancement of Xen's scheduler for MapReduce workloads
Hui Kang, Yao Chen, Jennifer L. A. Wong, Radu Sion, Jason Wu · 2011
As the trends move towards data outsourcing and cloud computing, the efficiency of distributed data centers increases in importance. Cloud-based services such as Amazon's EC2 rely on virtual machines (VMs) to host MapReduce clusters for large data processing. However, current VM scheduling does not provide adequate support for MapReduce workloads, resulting in degraded overall performance. For example, when multiple MapReduce clusters run on a single physical machine, the existing VMMscheduler does not guarantee fairness across clusters.