2. 







Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

3. 

 pay-as-you-go

Web
HPC
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

4.  HPC

 Amazon Web Services

 Science Cloud
my resource
others
VM
VM
VM
VM
VM Monitor/resource manager
cloud users
provided virtual
compute resources
over HTTP, SSH
black box
cloud physical infrastructure = Data Center
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

5.  Compute-intensive Applications [Walker ’08]



HPC
 Data-intensive Applications
 [Deelman et al. ‘’08]
[Palankar et al. ‘’08]


I/O
* Clouds
Dynamic Load-Balanced Multicast for Data-Intensive Applications onhttp://montage.ipac.caltech.edu/

6. 










cloud storage
cloud users
only once
cloud compute resources
high network transfer charge
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

7. 










Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

8. 








Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

9. B
C
T= + M/B + ….
• Optimal Tree
clusters and grids
•
•
WAN
•
A Bandwidth(B-C) = 800Mbps
Latency(B-C) = 2ms •
D







&
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

10.  structured
(ALM)
 [Castro et al. ’02] application-level multicast
 [Castro et al. ’03]
structured overlay
 [Cohen et al. ’03]









Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

11. Algorithm features cluster P2P
multicast topology spanning tree overlay
communication type push pull
network performance high row
network proximity dense sparse
node-to-node performance homo. hetero.
topology stable unstable
adaptability for dynamic change bad good
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

12. total I/O throughput
I/O bandwidth bottleneck
bucket
nodes
 flat tree algorithm



Flat Tree
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

13.  experiment 1:


 experiment 2:






Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

14. throughput
70
10MB
60
100MB
frequency (%) 50
1GB
40
iterations = 1000
30
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12
download throughput from S3 (MB/sec)



Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

15. comp. time & throughput
slowest average fastest
300 40
8 nodes
8nodes (N = 80)
Completion Time (sec)
250 35
16nodes (N = 160)
30
frequency (%)
200 32nodes (N = 320)
25
150 20
file size: 1GB
15
100
10
50 5
0 0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 11
runs download throughput from S3 (MB/sec)
 ( )


 1 ( )


Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

16. total throughput
70 200
Total Throughput (MB/sec)
total throughput (MB/sec)
node 1 180 187
60
node 2 160
50 140
node 3
40 120
node 4
100
30 node 5 106
80
20 node 6 60
10 node 7 40 52
node 8 20
0 0
1 2 3 4 5 6 7 8 9 10 8nodes 16nodes 32nodes
runs



 S3



Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

17.  S3



 ,




Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

18. 
















I/O,
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

19. 







Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

20. 








Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

21. cluster P2P clouds
multicast topology spanning tree overlay tree + overlay
communication type push pull pull
network performance high row middle
network proximity dense sparse dense
node-to-node performance homo. hetero. hetero.
topology stable unstable (un)stable
adaptability for dynamic bad good good
change
cluster
multicast
proposed multicast
algorithm on clouds
P2P
multicast
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

22. 
 
 
 Scatter (phase 1) Allgather (phase 2)
[van de Geijn algorithm ’93]




bucket
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

23. phase 1
 32KB

iP (i + 1)P
 Ri = ( , − 1)
N N

32KB
32KB
expression
meaning
P
N
Ri
i ID
node 0
node 1
node N-1
Wi
i
i, j
(0 i, j < N)
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

24. phase 2




 BitTorrent-like
 possession(i)
]
 p request(p)
]
 p have(p)
]
 update( possession(i) ) ]
pos. list
update
update
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

25. phase 1 (1/2)

 Download Work Stealing


32KB
32KB
node 0
node 1
node N-1
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

26. phase 1 (2/2)
1) steal request

2) divide current work
 i j
j
3) send new work list

 j
assigned download pieces
already downloaded
2 MB/sec not yet download
2) divide current work
slow node j
Wj = W ∗ Bj /(Bi + Bj )
1) steal request
3) send new work list
= 5 ∗ 2/(8 + 2) = 1
Wi = W ∗ Bi /(Bi + Bj )
fast node i
8 MB/sec = 5 ∗ 8/(8 + 2) = 4
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

27.  non-steal, steal algorithm flat tree
 (completion time) (stability)
 (node scalability)
 (performance analysis)



CPU
memory
HDD
price
small
1 ECU (1 core)
1.7 GB
160 GB
$0.10/hour
http://aws.amazon.com/ec2/instance-types/
ECU : EC2 Compute Unit 1.0 ~ 1.2 GHz
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

28. completion time and stability
non-steal algorithm
steal algorithm
slowest average fastest slowest average fastest
140 140
completion time (sec)
completion time (sec)
120 120
100 100
80 80
60 60
40 40
20 20
0 0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
runs runs
 1GB 8
 non-steal, steal flat tree




Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

29. node scalability
flat tree non-steal steal
10
total throughput (MB/sec)
9
8
7
6
5
4
3
2
1
0
4nodes 8nodes 16nodes 32nodes
 1GB





Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

30. finer analysis of our algorithms
phase 1 (steal) phase 1 (non-steal) steal algorithm
12 phase 2 (steal) phase 2 (non-steal) 160 phase 2
average throughput (MB/sec)
140 phase 1
10
completion time (sec)
120 40
8
100
70
6 80
81
4 60 95 103
102
2 40
55.5
20
0 29
15 10
0
4nodes 8nodes 16nodes 32nodes
2nodes 4nodes 8nodes 16nodes 32nodes
 non-steal

 steal




Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

31. 







Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds

32. 


Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds