DOC PREVIEW
Berkeley COMPSCI 268 - Improving MapReduce Performance in Heterogeneous Environments

This preview shows page 1-2-3-4-5-6 out of 18 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 18 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 18 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 18 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 18 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 18 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 18 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 18 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

Improving MapReduce Performance in Heterogeneous EnvironmentsMotivationResultsOutlineWhat is MapReduce?Fault Tolerance in MapReduceHeterogeneity in Virtualized EnvironmentsDisk Performance Heterogeneity ExperimentBackup Task Scheduling in HadoopProblems in Heterogeneous EnvironmentProgress Rate ApproachesExampleSlide 13Our Scheduler: LATELATE DetailsEvaluationConclusionQuestions?UC BerkeleyImproving MapReduce Performance in Heterogeneous EnvironmentsMatei Zaharia, Andy Konwinski,Anthony Joseph, Randy Katz, Ion Stoica University of California at BerkeleyMotivation•MapReduce programming model growing in popularity–Open-source implementation, Hadoop, used at Yahoo, Facebook, CMU, Berkeley,…•Virtualized computing services like Amazon EC2 provide on-demand compute power, but less control over performanceResults•Main challenge for Hadoop on EC2 was node performance heterogeneity•Designed heterogeneity-aware scheduler that improves response time up to 2xOutline•MapReduce background•The challenge of heterogeneity•LATE: a heterogeneity-aware schedulerWhat is MapReduce?•Programming model to split computations into independent parallel tasks–Map tasks filter data set–Reduce tasks aggregate values by key•Goal: hide the complexity of distributed programming and fault toleranceFault Tolerance in MapReduce•Nodes fail  re-run tasks•Nodes very slow (stragglers)  launch backup copies of tasks•How to do this in heterogeneous env.?Heterogeneity in Virtualized Environments•VM technology isolates CPU and memory, but disk and network are shared–Full bandwidth when no contention–Equal shares when there is contention•2.5x I/O performance difference on EC2Disk Performance Heterogeneity ExperimentBackup Task Scheduling in Hadoop•Scheduler starts all primary tasks, then looks for tasks to back up•Tasks report “progress score” from 0 to 1•Backup launched if progress < avgProgress - 20%Problems in Heterogeneous Environment•Too many backups (trash shared resources)•Wrong tasks may be backed up•Backups may be placed on slow nodes•Tasks never backed up if progress > 80%•Result: 80% of reduces backed up in some experiments, network overloadedProgress Rate Approaches•Compute average progress rate, back up tasks that are “far enough” below this•Problems:–How long to wait for statistics?–Can still select the wrong tasksExampleTime (min)Node 1Node 2Node 33x slower1.9x slower1 task/minExampleNode 1Node 2Node 3What if the job had 5 tasks?time left: 1 mintime left: 1.8 min2 minTime (min)Should back up node 3’s taskOur Scheduler: LATE•Insight: back up the task with the largest estimated finish time–“Longest Approximate Time to End”•Sanity thresholds:–Cap backup tasks to ~10%–Launch backups on fast nodes–Only back up tasks that are sufficiently slowLATE Details•Estimating finish time:•Thresholds:–25th percentiles for slow node/task, 10% cap–Sensitivity analysis shows robustnessprogress score execution timeprogress rate =1 – progress scoreprogress rateestimated time left =Evaluation•EC2 experiments (3 job types, 200 nodes)•Experiments in small controlled testbed•Contention through VM placement•Results:–2x better response time if there are stragglers –30% better response time when no stragglersConclusion•Heterogeneity is a challenge for parallel applications, and is getting more important•Lessons for scheduling backup tasks:–Detecting slow nodes isn’t enough; do it early–Pick tasks which hurt response time the most–Be mindful of shared resources•2x improvement using simple


View Full Document

Berkeley COMPSCI 268 - Improving MapReduce Performance in Heterogeneous Environments

Documents in this Course
Lecture 8

Lecture 8

33 pages

L-17 P2P

L-17 P2P

50 pages

Multicast

Multicast

54 pages

Load more
Download Improving MapReduce Performance in Heterogeneous Environments
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Improving MapReduce Performance in Heterogeneous Environments and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Improving MapReduce Performance in Heterogeneous Environments 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?