We present a system for allocating resources in shared data and compute clusters that
improves MapReduce job scheduling in three ways. First, the system uses regulated and …

We present a resource-aware scheduling technique for MapReduce multi-job workloads that
aims at improving resource utilization across machines while observing completion time …

Sharing a MapReduce cluster between users is attractive because it enables statistical
multiplexing (lowering costs) and allows users to share a common large data set. However …

MapReduce and Hadoop represent an economically compelling alternative for efficient
large scale data processing and advanced analytics in the enterprise. A key challenge in …

Large-scale MapReduce clusters that routinely process petabytes of unstructured and semi-
structured data represent a new entity in the changing landscape of clouds. A key challenge …

Cloud computing offers an attractive option for businesses to rent a suitable size
MapReduce cluster, consume resources as a service, and pay only for resources that were …

This paper presents a new MapReduce cloud service model, Cura, for provisioning cost-
effective MapReduce services in a cloud. In contrast to existing MapReduce cloud services …

We present Purlieus, a MapReduce resource allocation system aimed at enhancing the
performance of MapReduce jobs in the cloud. Purlieus provisions virtual MapReduce …

A typical MapReduce cluster is shared among different users and multiple applications. A
challenging problem in such shared environments is the ability to efficiently control resource …

The elapsed time of a parallel job depends on the completion time of its longest running
constituent. We present a static load balancing algorithm that distributes work evenly across …