optimizing mapreduce provisioning in the cloud

University of Minnesota

Optimizing MapReduce Provisioningin the Cloud

Michael Cardosa, Aameek Singh†,Himabindu Pucha†, Abhishek Chandra

http://www.cs.umn.edu/~cardosa

Department of Computer Science, University of Minnesota

†IBM Almaden Research Center

University of Minnesota

MapReduce Provisioning Problem Platform:

Virtualized Cloud Environment, which enables

Virtualized MapReduce Clusters Several MapReduce Jobs from different

users Goal: Optimize system-wide metrics, such

as: throughput, energy, load distribution, user costs

Problem: At the Cloud Service Provider level, how can we harvest opportunities to increase performance, save energy, or reduce user costs? 2

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MapReduce Platform: Hadoop Open-source implementation of MapReduce

distributed computing framework Used widely: Yahoo, Facebook, NYT, (Google)

InputData

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Hadoop Clusters

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Distributed data Replicated chunks

Distributed computation Map/reduce tasks

Traditional: Dedicated physical nodes

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Virtual Hadoop Clusters

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Run Hadoop on top of VMs E.g.: Amazon Elastic MapReduce =

Hadoop+AmazonEC2

Server Pool

VM Pool

Hadoop Processes

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Roadmap Intro & Problem Platform Overview Spatio-Temporal Insights for

Provisioning Building Blocks for MapReduce

Provisioning Case Study: Performance optimization Case Study: Energy optimization

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Spatio-Temporal Insights for Provisioning

Initial Focus: Energy Savings Goal: Minimize energy usage

Energy+cooling ~ 42% of total cost [Hamilton08]

Problem: How to place the VMs on available physical servers to minimize energy usage? Minimize Cumulative Machine Uptime (CMU)

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VM Placement: Spatial Fit

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Job 1 Job 2 Job 3 Job 4

Co-Place complementary

workloads

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Which placement is better?

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20min

10min

100min

20min20min

20min

SHUTDOWN SHUTDOWN

A B

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Time Balancing

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20 25

90

20 25 20 25

20 25

30

20 25

30

20 25

30

Time Balance

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Building Blocks for Provisioning

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Objective-drivenresource provisioning

MapReduce Jobs

Jobprofiling

Clusterscaling Migration

Cloud Execution Environment

Initial Provisioning Continuous Optimization

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Building Blocks for Provisioning Job Profiling: MapReduce job runtime

estimation Based on number of VMs allocated to job Based on input data size Offline and Online Profiling

Cluster Scaling: Changing number of VMs allocated to a particular MapReduce job Affects runtime of job; relies on Job Profiling

model Migration: Useful for continuous

optimization Load balancing, VM consolidation

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Job Profiling: Runtime Estimation Based on Number of VMs

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Job Profiling: Runtime Estimation Based on Input Data Size

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Job Profiling: Runtime Estimation Online Profiling: Additional refinement

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Cluster Scaling Increasing allocated resources (typical):

Add additional VMs to join virtualized Hadoop cluster

Job performance increases, runtime decreases

E.g, for Time Balancing: Energy reasons E.g, Load Balancing and Deadlines:

Performance

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Cluster Scaling: Time Balancing

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20 25

90

20 25 20 25

20 25

30

20 25

30

20 25

30

Time Balance

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Roadmap Intro & Problem Platform Overview Spatio-Temporal Insights for

Provisioning Building Blocks for MapReduce

Provisioning Case Study: Performance optimization Case Study: Energy optimization

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Case Study: Performance & Deadlines

Goal: Meet deadlines for MapReduce jobs Determine initial allocation accurately Dynamically adjust allocation to meet

deadline if necessary Monitoring: Use offline profiling to estimate

number of VMs needed based on past performance

Actuation: Online profiling: Trigger points to invoke cluster scaling

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Case Study: Energy Savings Goal: Minimize energy consumption from

the execution of a large batch of MapReduce jobs Energy+cooling ~ 42% of total cost

[Hamilton08] Pass energy savings on to users

Problem: How to place the VMs on available physical servers to minimize energy usage? Minimize Cumulative Machine Uptime (CMU)

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Case Study: Energy Savings Use Job Profiling to place similar-runtime

VMs together for initial provisioning Use Job Profiling to adjust number of

VMs in each cluster to adjust runtimes if needed

Monitoring: Online profiling to determine when energy could be saved by using migration or cluster scaling

Actuation: Use Cluster Scaling or Migration to dynamically adjust for inaccuracies/unknowns in initial provisioning

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Conclusion Framework: Building blocks (STEAMEngine)

for the optimization of MapReduce provisioning from a cloud service provider perspective

Preliminary evaluations to validate usefulness of each building block

Approaches for applying building blocks to meet specific goals, e.g. performance, energy

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Thank you! Questions?

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Job Profiling: Runtime Estimation Based on Number of VMs

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Cluster Scaling Increasing allocated resources (typical):

Add additional VMs to join virtualized Hadoop cluster

Job performance increases, runtime decreases

E.g, for Time Balancing: Energy reasons E.g, Load Balancing and Deadlines:

Performance

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