MapReduce/Hadoop production clusters exhibit heavy-tailed characteristics for job
processing times. These phenomena are resultant of the workload features and the adopted …

As MapReduce is becoming ubiquitous in large-scale data analysis, many recent studies
have shown that the performance of MapReduce could be improved by different job …

MapReduce is a popular computing paradigm for large-scale data processing in cloud
computing. However, the slot-based MapReduce system (eg, Hadoop MRv1) can suffer from …

This paper presents Natjam, a system that supports arbitrary job priorities, hard real-time
scheduling, and efficient preemption for Mapreduce clusters that are resource-constrained …

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

MapReduce is becoming a popular programming model for large-scale data processing in
cloud computing environments. Hadoop MapReduce is the most popular open-source …

The MapReduce paradigm and its open source implementation Hadoop are emerging as an
important standard for large-scale data-intensive processing in both industry and academia …

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 …

For MapReduce/Hadoop, map and reduce phases exhibit fundamentally distinguishing
characteristics. Additionally, these two phases admit complicated and tight dependency on …

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 …