Experience from an operational Map-Reduce cluster reveals that outliers significantly
prolong job completion. e causes for outliers include run-time contention for processor …

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 …

The MapReduce model uses a barrier between the Map and Reduce stages. This provides
simplicity in both programming and implementation. However, in many situations, this barrier …

MapReduce and Spark are two very popular open source cluster computing frameworks for
large scale data analytics. These frameworks hide the complexity of task parallelism and …

We propose new adaptive runtime techniques for MapReduce that improve performance
and simplify job tuning. We implement these techniques by breaking a key assumption of …

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 …

To improve data availability and resilience MapReduce frameworks use file systems that
replicate data uniformly. However, analysis of job logs from a large production cluster shows …

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

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 a programming model and an associated implementation for processing and
generating large datasets that is amenable to a broad variety of real-world tasks. Users …