As the demands for big data analytics keep growing rapidly in scientific applications and
online services, MapReduce and its open-source implementation Hadoop gained popularity …

Hadoop is a popular implementation of the MapReduce framework for running data-
intensive jobs on clusters of commodity servers. Shuffle, the all-to-all input data fetching …

As clusters continue to grow in size and complexity, providing scalable and predictable
performance is an increasingly important challenge. A crucial roadblock to achieving …

This paper presents a comprehensive review of fairness in resource allocation and its
foundation. Fairness is applied when the resources divided on multiple demands are limited …

Schedulers are critical in enhancing the performance of MapReduce/Hadoop in presence of
multiple jobs with different characteristics and performance goals. Though current …

MapReduce is a widely accepted framework for addressing big data challenges. Recently, it
has also gained broad attention from scientists at the US leadership computing facilities as a …

Cloud providers can form cloud federations by pooling their resources together to balance
their loads, reduce their costs, and manage demand spikes. However, forming cloud …

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 …

Resource sharing in virtualized environments have been demonstrated significant benefits
to improve application performance and resource/energy efficiency. However, resource …

Hadoop emerged as the de facto state-of-the-art system for MapReduce-based data
analytics. The reliability of Hadoop systems depends in part on how well they handle …