1. Matchmaking: A New MapReduce Scheduling Technique
Chen He Dr. Ying Lu Dr. David Swanson

2. Problem Statement
MapReduce cluster scheduling algorithm becomes increasingly important
Efficient MapReduce scheduler must avoid unnecessary data transmission
We will focus on decreasing data transmission in a MapReduce cluster

3. Contributions
Build a matchmaking algorithm to improve data locality of Hadoop MapReduce jobs
MatchMaking algorithm lead to higher data locality rate and shorter map task response time
We substitute Delay algorithm with MatchMaking algorithm in Fair-sharing scheduler and also obtain better performance

4. Outline Background Delay Algorithm MatchMaking algorithm Evaluation
Conclusion
Questions

5. Background Hadoop FIFO scheduler
Scheduler searches local tasks in the first job and assign them
If no local task in the first job, a non-local task of the first job will be assigned
Strict FIFO job order is followed

6. Background
Hadoop FIFO scheduler

7. Background
Hadoop FIFO scheduler

8. Background
Hadoop FIFO scheduler

9. Background
Hadoop FIFO scheduler

10. Background
Hadoop FIFO scheduler

11. Background Hadoop FIFO scheduler deficiencies
On the node side, strict FIFO job order reduces data locality
On the job side, FIFO can not provide a fair opportunity for each worker node

12. Delay Algorithm
Driven by Facebook events log saved in their Hadoop data warehouse
Hadoop default FIFO scheduler results in unnecessarily long job response time and lack of fairness in resource sharing
Focus on two points: fair sharing and data locality

13. Delay Algorithm Workload*
Bin
#Maps
%Jobs at Facebook
#Maps in Benchmark
# of jobs in Benchmark 1
39% 38 2
16% 16 3
3-20
14% 10 14 4
21-60 9% 50 8 5
61-150 6%
100 6
200 7 4%
400
800 9
>1501 3%
4800
*Matei Zaharia et al “Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling”

14. Delay Algorithm Fairness: Data locality
Task execution percentage between jobs
groups
users
Data locality
For Map stage, a map task is running on a node that contains its input data
For Reduce stage?

15. Fairness VS. Data locality
Delay Scheduling
Fairness VS. Data locality

16. Delay Algorithm
Fair-sharing principle-hierarchical principle

17. Delay Scheduling-including rack locality

18. Delay Algorithm Relax the strict job order
Scheduler can search other jobs in the job queue to find a local task
Maximum Delay Time (MDT) for a job to avoid starvation
MDT is a user defined maximum time that the scheduler can delay a job from assigning its non-local map tasks

19. Delay Algorithm

20. Delay Algorithm

21. Delay Algorithm

22. Delay algorithm

23. Delay algorithm

24. Delay algorithm

25. Delay Algorithm Properties
MDT decides data locality rate
Rl is an increasing function of MDT but with a ceiling value “1”
However, average response time

26. Delay Algorithm Deficiency
To achieve best response time, we need to
vary the MDT value
different types of jobs
different cluster sizes
different job execution orders

27. Outline Background Delay Algorithm MatchMaking algorithm Evaluation
Conclusion
Questions

28. MatchMaking Algorithm
Relax strict job order
search all jobs in the queue for local tasks
To give every node a fair chance to grab its local tasks
when a node fails to find a local task for the first time in a row, no non-local task will be assigned to it
when a node fails to find a local task for the second time in a row, a non-local task will be assigned to it
A node can be assigned at most one non-local task in every heartbeat interval

29. MatchMaking Algorithm

30. MatchMaking Algorithm

31. MatchMaking Algorithm

32. MatchMaking Algorithm

33. MatchMaking Algorithm

34. MatchMaking Algorithm

35. Outline Background Delay Algorithm MatchMaking algorithm Evaluation
Conclusion
Questions

36. Evaluation Environment Test cases Metrics Hardware
1 head node with 2 AMD Optron 2.2GHz 64bit, 8GB Mem, 1Gbps Ethernet
30 worker nodes with same CPUs and network but 4GB Mem
Software
Hadoop 0.21
Redhat Linux CentOS 5.5
Test cases
Loadgen
Wordcount
Metrics
Locality Rate
Average Response Time

37. Evaluation Hadoop Configuration HDFS MapReduce
Block size is128MB
100 Blocks evenly distributed in 30 worker nodes
Replication number is 2
MapReduce
2 map slots and 1 reduce slot for each worker node
Facebook production workload*
*Matei Zaharia et al “Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling”

38. Evaluation FIFO Scheduler Fair-sharing Scheduler
Default locality policy
Delay policy
Matchmaking policy
Fair-sharing Scheduler

39. Evaluation
FIFO scheduler locality rate
loadgen wordcount

40. Evaluation
FIFO scheduler MTART
loadgen wordcount

41. Evaluation
Fair sharing scheduler locality rate

42. Evaluation
Fair sharing scheduler response time

43. Conclusion
We create MatchMaking algorithm to improve MapReduce scheduler’s data locality without tuning
It obtains good performance in a middle size cluster with Facebook production workload
It can be easily integrated with other scheduler like FIFO or Fair-sharing scheduler

44. Disscussion
Data locality in the Reduce stage

45. Discussion
Performance in a large cluster and uneven distributed environment
Large cluster may have long hearbeat interval
Large block size
ResponseTime=QueuingTime+DataLoadingTime+DataProcessTime
More replicas
Data blocks may not be evenly distributed
Hotspot

46. Discussion If the job queue is very long.
Set a parameter MaxJobConsidered
Priorities

47. Discussion
Anything else?

48. Questions
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