1. Replica Placement Heuristics of Application-level Multicast
Chia-Hsing Yu
Jiahua He
CSE of UCSD

2. Project Presentation of CSE 222A
Outline
Multicast and RMX
Model and Heuristics
Simulation and Results
Conclusion and Future Work
2019/4/8
Project Presentation of CSE 222A

3. Application-level Multicast
Goal
Distribute Contents to Many Clients
Problem
How to reduce the load of the central server?
How to reduce the response time of requests?
Replication at different servers
2019/4/8
Project Presentation of CSE 222A

4. RMX: Reliable Multicast proXy
TCP
SRM: Reliable IP Multicast
2019/4/8
Project Presentation of CSE 222A

5. Project Presentation of CSE 222A
RMX
Semantic reliability
information  representation of information
Sender can lower the stream resolution if the network load is heavy
2019/4/8
Project Presentation of CSE 222A

6. Project Presentation of CSE 222A
Existing Problems
Only sources, no replicas
No request, only recovery request
Static RMXs in network
Static configuration of data groups
2019/4/8
Project Presentation of CSE 222A

7. Project Presentation of CSE 222A
Related works
Replication in unstructured P2P (Princeton)
Owner, Path, Random
PAST(Microsoft and Rice)
Nodes with similar id’s
OceanStore (Berkeley)
On or near the clients
Focus on persistent storage with versions
Chain (Cornell)
Machines with replicas of a same file form a chain
Focus on availability
2019/4/8
Project Presentation of CSE 222A

8. Project Presentation of CSE 222A
Model and Heuristics
Fixed sources and dynamic replicas
Streaming multicast on demand
No replication
Baseline
Replication on path
FIFO
LRU
Color
2019/4/8
Project Presentation of CSE 222A

9. Project Presentation of CSE 222A
Baseline
Only sources, no replicas
Learning bridge scheme to search
Learn routing information from incoming data
Soft state: periodically refresh
Request suppression
Ideal condition: no loss
2019/4/8
Project Presentation of CSE 222A

10. Project Presentation of CSE 222A
FIFO and LRU
Replication on path
Broadcast to search
FIFO:
Remove the oldest one if no space
LRU:
Order the files by last usage
2019/4/8
Project Presentation of CSE 222A

11. Project Presentation of CSE 222A
Color
Graph coloring
Neighbors with different colors (files) from mine
Can get more different files from neighbors
Remove the file with nearest replica
Visiting Frequency
More frequently visited, more possible to be visited
Cost function: dist * freq
dist: distance to the nearest replica
freq: visiting frequency
Upper bound of the cost if removed
2019/4/8
Project Presentation of CSE 222A

12. Project Presentation of CSE 222A
Simulator
Event-driven Simulator
New Event
New Event
New Event
Event
Handler
Min
Heap
Earliest Event
2019/4/8
Project Presentation of CSE 222A

13. Project Presentation of CSE 222A
Simulator(2)
Stream-level Simulation
SIM_SEND_STREAM( bit rate, length )
Input
Network Topology
Host Resources
Stream Sources
User Requests
2019/4/8
Project Presentation of CSE 222A

14. Experiment Configuration
Network Topology
Binary Tree
Host Resources
127 hosts (data groups)
Hard disk size variable
Stream Sources
1270 sources (average 10 per host)
500 Kbps, 8000 seconds each
Randomly distributed
User Request
Total number variable
Experiment Span
100 hours
2019/4/8
Project Presentation of CSE 222A

15. Experiment Configuration (2)
Variances
Number of requests: 211 ~ 218
Hard disk size: 8G ~ 128G
Metrics
Client view average response time
Server view load (number of streams per RMX) load balance (standard deviation of load)
System view throughput
2019/4/8
Project Presentation of CSE 222A

16. Client View Avg. Response Time vs. # of Requests
About 30% improvement
2019/4/8
Project Presentation of CSE 222A

17. Client View Avg. Response Time vs. Disk Size
Disk size outperforms replication strategy
2019/4/8
Project Presentation of CSE 222A

18. Server View Avg. # of Streams vs. # of Requests
About 50% improvement
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Project Presentation of CSE 222A

19. Server View S.D. # of Streams vs. # of Requests
About 50% improvement
2019/4/8
Project Presentation of CSE 222A

20. Server View Avg. # of Streams vs. Disk Size
Disk size outperforms replication strategy
2019/4/8
Project Presentation of CSE 222A

21. Server View S.D. # of Streams vs. Disk Size
Disk size outperforms replication strategy
2019/4/8
Project Presentation of CSE 222A

22. System View Throughput vs. # Requests
About 25% improvement
2019/4/8
Project Presentation of CSE 222A

23. System View Throughput vs. Disk Size
Upper bound
System View Throughput vs. Disk Size
2019/4/8
Project Presentation of CSE 222A

24. Project Presentation of CSE 222A
Contributions
Implement and analyze Baseline, FIFO, LRU algs
Propose and verify Color heuristics
Avg. response time: up to 30% improvement
Load: up to 50% improvement
Load balance: up to 50% improvement
Throughput: up to 25% improvement
2019/4/8
Project Presentation of CSE 222A

25. Project Presentation of CSE 222A
Future Works
Biased requests
Heterogeneous environment (hosts, links, streams)
Random forward
More sophisticated heuristics
Experiment in real environment
2019/4/8
Project Presentation of CSE 222A