DYNAMICALLY CONTROLLING NODE LEVEL PARALLELISM IN HADOOP

PRESENTED BY -

AMARJIT SINGHVIGNESH VENKATARANANUJAM

NODE LEVEL PARALLELISM

NodeMemory

5 GB

Container1 GB 1 GB

1 GB

1 GB

1 GB

1 GB

• MAXIMUM - 5 MAP/ REDUCE TASKS

P- 150

P-2

Increase /Decrease of CCS

DYNAMIC CONTROLLING

5 CCS

Manual Tuning

Time consuming / Performance Implications

Dynamic Tuning

Faster / Better Performance

DYNAMIC TUNING OF CCS USING FEEDBACK CONTROLLERS

+ ccs

- ccsPD controllerWaterlevel PD + Pruning

CPU %

Blocked I/O Processes

Context Switch

Error = Reference value – Value

User_cpu Proc_blocked Ctxt

Value

YARN ARCHITECTURE* Config Files (Momory + Virtual core Limit )

Itration = 1

CCS = 7

compute CCS

Itration = 2

CCS = 4

RM

NM

NM

NM

1

1......N

SUSPENDING CONTAINERS – PROPOSED METHOD

RM

NM

NM

NM

Wait queue

Ready Queue

Uses existing ccs to allocate containers.

Periodically compute CCS value - API

IF { New CCS < Old CCS } Suspend Containers

IF { New CCS > Old CCS & Suspended containers }

Then { Resume old containers before new containers spawn }

IF { New CCS > Old CCS & no Suspended containers }

Then { assign new containers }

CCS Alloted = 7CCS Alloted = 14CCS Alloted =21

CCS Alloted = 14

WATER-LEVEL CONTROLLER

1

0CCS

UB

LB

Water

User_cpu Proc_blocked 0 : Lower Thresh-hold 1: Upper Thresh-holdCtxt

1 0

CCS

Continuous

Increase

Continuous

Decrease

PD CONTROLLER

User_cpu Proc_blocked Score Ctxt

CCS

Scor

e

ccs =10ccs =12

ccs=14Current Timeline

Current error

[ E(J) ]

Change in error

[ E(J) – E(J-1)]

Proportionate constant

kP *

Derivative constant

kD *+CCS =

Error = score

ccs

scoreCCS

Experimental Setup

Ten IBM Power PC Machines

10 GBPS Ethernet network B/w RM & NM’s

16 cores 64 CPU Threads 124 GB RAM

For each node

RM

NM 9

NM 2

NM 1

1

Applications used for Testing

Applications are selected based on two factors CPU Utilization IO Demand

Performance Comparison

• Default Configuration is at least 50% slower for all applications

• All three dynamic approaches are much better than best practices (7-31% better)

• PD is better than WaterLevel and PD+pruning except for grep application

Tuning Methods to be Compared• Default• Best practice• Three Dynamic Controlling Methods (PD,WL &

PD+pruning)Table: Relative Comparison of map completion time for various tuning methods

Performance Comparison

• Default, best case, and exhaustive search have static CCS value

• Among dynamic approaches PD and WaterLevel changes CCS

• PD+pruning changes CCS initially, but stabilizes CCS to a fixed value after 350 second mark

Fig : Change of CCS value of all tuning approaches

• Dynamic tuning achieves the most satisfactory performance as well as CCS responsiveness

Resource Usage Comparison

Default Tuning Best practice Tuning

PD Tuning

Conclusion

Does not under utilize resources

In Performance comparison PD based dynamic controller showed improvement compared to best Practice method and Default method

Dynamic approach change the CCS value dynamically for efficient utilization of resources

Dynamic approach suspends the container when it has less CCS value which reduce CPU contention