1. Coded Caching in Information-Centric Networks
Zhuoqun Chen 陈卓群
SJTU
June 2015

2. ?
Coded Caching
ICN

3. Coded Caching
ICN

4. Roadmap to ICN Coded Caching in ICN Nonuniform Demands Coded Caching
Decentralized Coded Cache
Nonuniform Demands 1 2
Delay Sensitive 4 3
Coded Caching in ICN 5
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5. Uncoded Caching - Least Frequently Used
N=2 Files, K=1 Users, Cache Size M=1
PA=2/3
Populate the cache in low-traffic time
Server
PB=1/3
Cache the most popular file(s)
E[R]=PB=1/3
Average rate is the same as miss rate
User
Cache
Size One
LFU is optimum for one cache memory in the system.
LFU minimizes the miss rate.

6. Least Frequently Used Is this optimum? E[R]=1-(2/3)2=5/9
N=2 Files, K=2 Users, Cache Size M=1
PA=2/3
PB=1/3
E[R]=1-(2/3)2=5/9
Is this optimum?

7. Multicasting opportunity for users with different demand
Coded Caching Scheme
N=2 Files, K=2 Users, Cache Size M=1 A1 A2 B1 B2 A2
A2⊕B1 B1 A1 B1 A2 B2
Multicasting opportunity for users with different demand

8. Coded Caching Uncoded Caching Coded Caching [Maddah-Ali, Niesen 2012]
N Files, K Users, Cache Size M
Uncoded Caching
Caches used to deliver content locally
Local cache size matters
Coded Caching [Maddah-Ali, Niesen 2012]
The main gain in caching is global
Global cache size matters
(even though caches are isolated)

9. Centralized Coded Caching
N=3 Files, K=3 Users, Cache Size M=2
Maddah-Ali, Niesen, 2012
A12
A13
A23
B12
B13
B23
C12
C13
C23
Approximately Optimum
A23
B13
C12
A23⊕B13⊕C12
1/3
A12
A13
B12
B13
B23
C12
C13
C23
A23
Multicasting Opportunity between three users with different demands

10. Centralized Coded Caching
N=3 Files, K=3 Users, Cache Size M=2
A12
A13
A23
B12
B13
B23
C12
C13
C23
Centralized caching needs
Number and identity of the users in advance
In practice, it is not the case,
Users may turn off
Users may be asynchronous
Topology may time-varying (wireless)
A12
A13
B12
B13
B23
C12
C13
C23
A23
Question: Can we achieve similar gain without such knowledge?

11. ? ICN Coded Caching Key Features of ICN
Distributed nodes, not centralized
Various content popularity, not uniform
Asynchronous request and delivery, with deadline
Arbitrary network topology, not a shared link/tree

12. Roadmap to ICN Coded Caching in ICN Nonuniform Demands Coded Caching
Decentralized Coded Cache
Nonuniform Demands 1 2
Delay Sensitive 4 3
Coded Caching in ICN 5
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13. Decentralized Caching Scheme
N=3 Files, K=3 Users, Cache Size M=2 1 2 3 12 13 23
123 1 2 3 12 13 23
123 1 2 3 12 13 23
123
Delivery: Greedy linear encoding
Prefetching: Each user caches 2/3 of the bits of each file
- randomly,
- uniformly,
- independently. 2 1 ⊕ 23 13 12 ⊕ 3 2 ⊕ 3 1 ⊕ 1 12 13
123 2 12 23
123 3 13 23
123 1 12 13
123 2 12 23
123 3 13 23
123 1 12 13
123 2 12 23
123 3 13 23
123

14. Decentralized Caching

15. Decentralized Caching
Centralized Prefetching: 12 13 23 12 13 23 12 13 23
Decentralized Prefetching: 1 2 3 12 13 23
123 1 2 3 12 13 23
123 1 2 3 12 13 23
123

16. Comparison Uncoded Local Cache Gain: Proportional to local cache size
N Files, K Users, Cache Size M
Uncoded
Local Cache Gain:
Proportional to local cache size
Offers minor gain
Coded (Centralized): [Maddah-Ali, Niesen, 2012]
Global Cache Gain:
Proportional to global cache size
Offers gain in the order of number of users
Coded (Decentralized)

17. The proposed scheme is optimum within a constant factor in rate.
Theorem:
The proposed scheme is optimum within a constant factor in rate.
Can We Do Better?

18. Roadmap to ICN Coded Caching in ICN Nonuniform Demands Coded Caching
Decentralized Coded Cache
Nonuniform Demands 1 2
Delay Sensitive 4 3
Coded Caching in ICN 5
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19. Non-Uniform Demands Contradicting Intuitions:
More popular file  More caching memory
Symmetry of the prefetching  Tractable Analysis

20. Idea of Grouping Prefetching: Delivery:
Group the files with approximately similar popularities
Dedicate Memory Mi to group i.
Prefetching:
Apply decentralized prefetching within each group i, with memory budget of Mi
Delivery:
Apply coded delivery for users demanding file from one group. M1 M2 M3 M4
M1+M2+M3+M4=M

21. Observations Within each group same cache allocation
Files in different group  different cache allocation
Symmetry within each group  Analytically tractable
Losing coding between groups

22. Roadmap to ICN Coded Caching in ICN Nonuniform Demands Coded Caching
Decentralized Coded Cache
Nonuniform Demands 1 2
Delay Sensitive 4 3
Coded Caching in ICN 5
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23. Requests have Deadlines!
Merge Rule
First-fit
Perfect-fit

24. Can We Do Better?
Misfit function:
τ-Fit Threshold Rule

25. Roadmap to ICN Coded Caching in ICN Nonuniform Demands Coded Caching
Decentralized Coded Cache
Nonuniform Demands 1 2
Delay Sensitive 4 3
Coded Caching in ICN 5
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26. Simulation Hard to analyze theoretically One Shared link
Numerous links
Hard to analyze theoretically
Simulation

27. Simulation - Network Topology
1 Content Provider, 15 users, each with cache size:10
Simple Interest Forwarding Strategy: Flooding/Broadcast
Parameters:
20000 contents
Request Pattern:
Zipf distribution
α = 0.8

28. Results (1) Request Merging – Caching Efficiency

29. Results (2) Request Merging - Delay
Deadline = 10

30. Results (3) Request Merging – Delay Vs Cache size
Request rate = 10000/s

31. Conclusion To adapt coded caching to ICN Merge Rule
Use Decentralized algorithm
Group contents to deal with non-uniform request
Merge contents to enhance multicast efficiency
Consider delay
Merge Rule
Tradeoff between Delay and Caching gain

32. Discussion & Future Work
Simulation on networks with various Connectivity
Consider link failure, Congestion
Capacity of ICN with coded caching

33. Thanks everyone