Name
Abstract | ||
|---|---|---|
MapReduce systems face enormous challenges due to increasing growth, diversity, and consolidation of the data and computation involved. Provisioning, configuring, and managing large-scale MapReduce clusters require realistic, workload-specific performance insights that existing MapReduce benchmarks are ill-equipped to supply. In this paper, we build the case for going beyond benchmarks for MapReduce performance evaluations. We analyze and compare two production MapReduce traces to develop a vocabulary for describing MapReduce workloads. We show that existing benchmarks fail to capture rich workload characteristics observed in traces, and propose a framework to synthesize and execute representative workloads. We demonstrate that performance evaluations using realistic workloads gives cluster operator new ways to identify workload-specific resource bottlenecks, and workload-specific choice of MapReduce task schedulers. We expect that once available, workload suites would allow cluster operators to accomplish previously challenging tasks beyond what we can now imagine, thus serving as a useful tool to help design and manage MapReduce systems. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/MASCOTS.2011.12 | MASCOTS |
Keywords | Field | DocType |
processor scheduling,parallel processing,mapreduce,production mapreduce,workload,pattern clustering,mapreduce task scheduler,large-scale mapreduce cluster,mapreduce performance evaluation,realistic workloads,workload suites,workload-specific performance,mapreduce workloads,mapreduce benchmarks,large-scale mapreduce cluster management,resource allocation,mapreduce task schedulers,workload-specific resource bottlenecks,mapreduce performance,representative workloads,data handling,performance evaluation,performance,mapreduce workload,mapreduce benchmark,benchmark,mapreduce system,measurement,benchmark testing,production | Computer science,Workload,Parallel processing,Provisioning,Real-time computing,Resource allocation,Operator (computer programming),Group method of data handling,Vocabulary,Benchmark (computing),Distributed computing | Conference |
ISSN | ISBN | Citations |
1526-7539 | 978-1-4577-0468-0 | 219 |
PageRank | References | Authors |
7.80 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanpei Chen | 1 | 917 | 41.46 |
| Archana Ganapathi | 2 | 860 | 54.96 |
| Rean Griffith | 3 | 2185 | 99.68 |
| Randy H. Katz | 4 | 16819 | 3018.89 |