T Sandholm, K Lai - Proceedings of the eleventh international joint …, 2009 - dl.acm.org
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 …
S Tang, BS Lee, B He - IEEE Transactions on Cloud …, 2014 - ieeexplore.ieee.org
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 …
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 …
J Tan, X Meng, L Zhang - Proceedings of the 12th ACM SIGMETRICS …, 2012 - dl.acm.org
MapReduce/Hadoop production clusters exhibit heavy-tailed characteristics for job processing times. These phenomena are resultant of the workload features and the adopted …
We present a resource-aware scheduling technique for MapReduce multi-job workloads that aims at improving resource utilization across machines while observing completion time …
As organizations start to use data-intensive cluster computing systems like Hadoop and Dryad for more applications, there is a growing need to share clusters between users …
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 …
MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. Hadoop is an …