Jockey: guaranteed job latency in data parallel clusters
Data processing frameworks such as MapReduce [8] and Dryad [11] are used today in
business environments where customers expect guaranteed performance. To date …
business environments where customers expect guaranteed performance. To date …
MapReduce optimization using regulated dynamic prioritization
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 …
improves MapReduce job scheduling in three ways. First, the system uses regulated and …
Dynamicmr: A dynamic slot allocation optimization framework for mapreduce clusters
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 …
computing. However, the slot-based MapReduce system (eg, Hadoop MRv1) can suffer from …
[PDF][PDF] Job scheduling for multi-user mapreduce clusters
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 …
multiplexing (lowering costs) and allows users to share a common large data set. However …
Aria: automatic resource inference and allocation for mapreduce environments
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 data processing and advanced analytics in the enterprise. A key challenge in …
Delay tails in MapReduce scheduling
MapReduce/Hadoop production clusters exhibit heavy-tailed characteristics for job
processing times. These phenomena are resultant of the workload features and the adopted …
processing times. These phenomena are resultant of the workload features and the adopted …
Resource-aware adaptive scheduling for mapreduce clusters
We present a resource-aware scheduling technique for MapReduce multi-job workloads that
aims at improving resource utilization across machines while observing completion time …
aims at improving resource utilization across machines while observing completion time …
Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling
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 …
Dryad for more applications, there is a growing need to share clusters between users …
Balancing reducer skew in MapReduce workloads using progressive sampling
SR Ramakrishnan, G Swart, A Urmanov - Proceedings of the Third ACM …, 2012 - dl.acm.org
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 …
constituent. We present a static load balancing algorithm that distributes work evenly across …
[PDF][PDF] Improving MapReduce performance in heterogeneous environments.
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 …
applications such as web indexing, data mining, and scientific simulation. Hadoop is an …