Performance analysis of coupling scheduler for mapreduce/hadoop
For MapReduce/Hadoop, map and reduce phases exhibit fundamentally distinguishing
characteristics. Additionally, these two phases admit complicated and tight dependency on …
characteristics. Additionally, these two phases admit complicated and tight dependency on …
Coupling task progress for mapreduce resource-aware scheduling
Schedulers are critical in enhancing the performance of MapReduce/Hadoop in presence of
multiple jobs with different characteristics and performance goals. Though current …
multiple jobs with different characteristics and performance goals. Though current …
Coupling scheduler for mapreduce/hadoop
Current schedulers of MapReduce/Hadoop are quite successful in providing good
performance. However improving spaces still exist: map and reduce tasks are not jointly …
performance. However improving spaces still exist: map and reduce tasks are not jointly …
Omo: Optimize mapreduce overlap with a good start (reduce) and a good finish (map)
MapReduce has become a popular data processing framework in the past few years.
Scheduling algorithm is crucial to the performance of a MapReduce cluster, especially when …
Scheduling algorithm is crucial to the performance of a MapReduce cluster, especially when …
Dreams: Dynamic resource allocation for mapreduce with data skew
MapReduce has become a popular model for large-scale data processing in recent years.
However, existing MapRe-duce schedulers still suffer from an issue known as partitioning …
However, existing MapRe-duce schedulers still suffer from an issue known as partitioning …
A hybrid scheduling algorithm for data intensive workloads in a mapreduce environment
The specific choice of workload task schedulers for Hadoop MapReduce applications can
have a dramatic effect on job workload latency. The Hadoop Fair Scheduler (FairS) assigns …
have a dramatic effect on job workload latency. The Hadoop Fair Scheduler (FairS) assigns …
Workload characteristic oriented scheduler for mapreduce
Applications in many areas are increasingly developed and ported using the Map Reduce
framework (more specifically, Hadoop) to exploit (data) parallelism. The application scope of …
framework (more specifically, Hadoop) to exploit (data) parallelism. The application scope of …
Dynamic resource allocation for MapReduce with partitioning skew
MapReduce has become a prevalent programming model for building data processing
applications in the cloud. While being widely used, existing MapReduce schedulers still …
applications in the cloud. While being widely used, existing MapReduce schedulers still …
MTSD: A task scheduling algorithm for MapReduce base on deadline constraints
The previous works about MapReduce task scheduling with deadline constraints neither
take the diffenences of Map and Reduce task, nor the cluster's heterogeneity into account …
take the diffenences of Map and Reduce task, nor the cluster's heterogeneity into account …
Automatic task re-organization in MapReduce
MapReduce is increasingly considered as a useful parallel programming model for large-
scale data processing. It exploits parallelism among execution of primitive map and reduce …
scale data processing. It exploits parallelism among execution of primitive map and reduce …