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A Hybrid Scheduling Approach for
Scalable Heterogeneous Hadoop Systems
Authors:
Aysan Rasooli
Douglas G. Down
McMaster University
November 2012
Agenda
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• Introducing the Hadoop System
• Heterogeneity and Scalability in Hadoop
• Performance Issues of Existing Hadoop Schedulers
• Proposed Hybrid Scheduling System
• Evaluation
• Conclusion
MapReduce
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Hadoop System
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Heterogeneity and Scalability in Hadoop
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• Cluster
• Workload
• User
Hadoop Schedulers
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• FIFO
• Fair Sharing
• COSHH
Fair Sharing
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(Zaharia et al., 2010)
• Group jobs into “pools”
• Assign each pool a guaranteed minimum share
• Divide excess capacity evenly between pools
J1
Job Queue
J2
R4
R3
R2
R1
Fair Sharing
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• Goal: fast response times for small jobs, guaranteed service
levels for long jobs
• Considers Minimum Share satisfaction, Fairness
Drawbacks:
• Does not take into account locality
• Does not take into account heterogeneity
Classes, Suggested Classes for all
Resources
Execution Time of the New Job on
all Resources
Hadoop System
Task Scheduling Process
Queuing Process
Routing Process
Heartbeat New Job
Selected Jobs
Assigned Tasks
COSHH Scheduler
Hadoop System
Task Scheduling Process
Queuing Process
Routing Process
• Estimation of Job execution time across all resources
• Classify the jobs
• Calculate the best set of suggested job classes for each resource
• Select a job for the current free resource using suggested jobs of the Queuing Process
• Consider the fairness and the minimum share satisfaction in the system
COSHH Scheduler
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• Considering the heterogeneity in the Hadoop system
• Improves Mean Completion Time
• Considers:
• Minimum Share Satisfaction
• Fairness
• Locality
Performance Issues of Existing Schedulers
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Problem I. Small Jobs Starvation
FIFO :
J1
Job Queue
J2
R4
R3
R2
R1
Performance Issues of Existing Schedulers
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Problem II. Sticky Slots
Fair Sharing:
R4
R3
R2
R1
Job Fair
Share Running
Tasks
Job 1 2 2
Job 2 2 2
Job Fair
Share Running
Tasks
Job 1 2 1
Job 2 2 2
Performance Issues of Existing Schedulers
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Problem III. Resource and Job Mismatch
Storage
Unit
slice 1
slice 2
slice 3
Computation
Unit
slot 1
slot 2
slot 3
slot 4
slot 5
R1
Storage
Unit
slice 1
slice 2
slice 3
slice 4
slice 5
slice 6
Computation
Unit
slot 1
slot 2
R2
File 1:
File 2:
J2
Job Queue
J1
Storage
Unit
slice 1
slice 2
slice 3
Computation
Unit
slot 1
slot 2
slot 3
slot 4
slot 5
Storage
Unit
slice 1
slice 2
slice 3
slice 4
slice 5
slice 6
Computation
Unit
slot 1
slot 2
Problem I. Small Jobs Starvation
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FIFO :
Problem II. Sticky Slots
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Fair Sharing :
Problem III. Resource and Job Mismatch
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COSHH:
Performance Issues of Existing Schedulers
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Experimental Environment
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Real Hadoop Workloads
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(Chen et al., 2011)
Scalability Analysis- Results
Job Number Scalability
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Scalability Analysis- Results
Resource Number Scalability
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Scalability Analysis- Hybrid Scheduler
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Scalability Analysis- Hybrid Scheduler
Job Number Scalability
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Scalability Analysis- Hybrid Scheduler
Resource Number Scalability
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Conclusion
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• Performance Issues of Hadoop Schedulers:
• Small Jobs Starvation
• Sticky Slots
• Resource and Jobs Mismatch
• Propose a Hybrid Hadoop Scheduler
Thanks
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