This document discusses node level parallelism and dynamic controlling of container creation services (CCS) on nodes. It proposes using a proportional-derivative (PD) controller to dynamically tune CCS based on metrics like CPU utilization, blocked I/O processes, and context switches. The PD approach is shown to provide faster performance and better resource utilization compared to manual tuning or a static best practice CCS value. Experimental results on 10 node clusters demonstrate the PD approach improves application completion times by 7-31% over static approaches.

2. NODE LEVEL PARALLELISM
Node
Memory
5 GB
Container
1 GB
1 GB
1 GB
1 GB
1 GB
1 GB
•
P- 150
P-2
P-1
Increase /Decrease of CCS
DYNAMIC CONTROLLING
5 CCS
Manual Tuning
Time consuming / Performance Implications
Dynamic Tuning
Faster / Better Performance

3. + ccs
- ccs
PD
controller
Waterlevel
PD
+
Pruning
CPU %
Blocked I/O Processes
Context Switch
Error = Reference value – Value
User_cpu
Proc_blocked
Ctxt
Value

4. * Config Files (Momory + Virtual core Limit )
Itration = 1
CCS = 7
compute CCS
Itration = 2
CCS = 4
RM
NM
NM
NM
1
1
...
...
N

5. 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 = 7
CCS Alloted = 14
CCS Alloted =21
CCS Alloted = 14

6. 1
0CCS
UB
LB
Water
User_cpu
Proc_blocked 0 : Lower Thresh-hold
1: Upper Thresh-hold
Ctxt
1
0
CCS
Continuous
Increase
Continuous
Decrease

7. User_cpu
Proc_blocked Score
Ctxt
CCS
Score
ccs =10
ccs =12
ccs=14
Current
Timeline
Current error
[ E(J) ]
Change in error
[ E(J) – E(J-1)]
Proportionate constant
kP *
Derivative constant
kD *+CCS =
Error = score
ccs
score
CCS

8. 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

9. Applications used for Testing
Applications are selected based on two factors
 CPU Utilization
 IO Demand

10. 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

11. 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

12. Resource Usage Comparison
Default Tuning Best practice Tuning
PD Tuning

13. 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