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Hong Tang updated MAPREDUCE-728: -------------------------------- Attachment: (was: mapreduce-728-20090918-4.patch) > Mumak: Map-Reduce Simulator > --------------------------- > > Key: MAPREDUCE-728 > URL: https://issues.apache.org/jira/browse/MAPREDUCE-728 > Project: Hadoop Map/Reduce > Issue Type: New Feature > Affects Versions: 0.21.0 > Reporter: Arun C Murthy > Assignee: Hong Tang > Fix For: 0.21.0 > > Attachments: 19-jobs.topology.json.gz, 19-jobs.trace.json.gz, > mapreduce-728-20090917-3.patch, mapreduce-728-20090917-4.patch, > mapreduce-728-20090917.patch, mapreduce-728-20090918-2.patch, > mapreduce-728-20090918-3.patch, mapreduce-728-20090918-5.patch, > mapreduce-728-20090918.patch, mumak.png > > > h3. Vision: > We want to build a Simulator to simulate large-scale Hadoop clusters, > applications and workloads. This would be invaluable in furthering Hadoop by > providing a tool for researchers and developers to prototype features (e.g. > pluggable block-placement for HDFS, Map-Reduce schedulers etc.) and predict > their behaviour and performance with reasonable amount of confidence, > there-by aiding rapid innovation. > ---- > h3. First Cut: Simulator for the Map-Reduce Scheduler > The Map-Reduce Scheduler is a fertile area of interest with at least four > schedulers, each with their own set of features, currently in existence: > Default Scheduler, Capacity Scheduler, Fairshare Scheduler & Priority > Scheduler. > Each scheduler's scheduling decisions are driven by many factors, such as > fairness, capacity guarantee, resource availability, data-locality etc. > Given that, it is non-trivial to accurately choose a single scheduler or even > a set of desired features to predict the right scheduler (or features) for a > given workload. Hence a simulator which can predict how well a particular > scheduler works for some specific workload by quickly iterating over > schedulers and/or scheduler features would be quite useful. > So, the first cut is to implement a simulator for the Map-Reduce scheduler > which take as input a job trace derived from production workload and a > cluster definition, and simulates the execution of the jobs in as defined in > the trace in this virtual cluster. As output, the detailed job execution > trace (recorded in relation to virtual simulated time) could then be analyzed > to understand various traits of individual schedulers (individual jobs turn > around time, throughput, faireness, capacity guarantee, etc). To support > this, we would need a simulator which could accurately model the conditions > of the actual system which would affect a schedulers decisions. These include > very large-scale clusters (thousands of nodes), the detailed characteristics > of the workload thrown at the clusters, job or task failures, data locality, > and cluster hardware (cpu, memory, disk i/o, network i/o, network topology) > etc. -- This message is automatically generated by JIRA. - You can reply to this email to add a comment to the issue online.