The document presents research on access strategies for network caching. It introduces the data store selection problem of determining which data stores to access based on indicators to minimize miss costs and access costs. The paper proposes modeling this as a knapsack problem and provides three approximation algorithms - DSKnap, DSPot, and DSPP. An evaluation on a real Wikipedia trace and CDN topology shows the DSKnap algorithm outperforms existing heuristics in total access costs across different miss rates and number of accessed locations.

1. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Access Strategies for Network Caching
Itamar Cohen∗, Gil Einziger∗, Roy Friedman+
and Gabriel Scalosub∗
∗Ben-Gurion University of the Negev, Israel
+Tehcnion, Israel
Infocom 2019

2. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Network caching
Source: Computer Networking: A Top-Down Approach, Kurose & Ross

3. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Network caching
Source: Computer Networking: A Top-Down Approach, Kurose & Ross

4. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Network caching
Source: Computer Networking: A Top-Down Approach, Kurose & Ross

5. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Network caching
Source: Computer Networking: A Top-Down Approach, Kurose & Ross
Which data stores should a user access?

6. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
The Data Store Selection (DSS) Problem

7. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Previous Work
Applications of network caching
5G networks [WCTKL’14]
Content delivery networks [BSHB’17]
Wide area networks [FCAB’00]
Space efficient indicators
BF [B’70] and its variants [BM’04, TRL’12]
Tiny table [EF’16]

8. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Previous Work
Applications of network caching
5G networks [WCTKL’14]
Content delivery networks [BSHB’17]
Wide area networks [FCAB’00]
Space efficient indicators
BF [B’70] and its variants [BM’04, TRL’12]
Tiny table [EF’16]
Access strategies
Access cheapest datastore with positive indication
[TRL’12]
Access all datastores with positive indications [FCAB’00]
The Bloom paradox [RK’15]

9. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Previous Work
Applications of network caching
5G networks [WCTKL’14]
Content delivery networks [BSHB’17]
Wide area networks [FCAB’00]
Space efficient indicators
BF [B’70] and its variants [BM’04, TRL’12]
Tiny table [EF’16]
Access strategies
Access cheapest datastore with positive indication
[TRL’12]
Access all datastores with positive indications [FCAB’00]
The Bloom paradox [RK’15]

10. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Contribution
Formal model for the Data Store Selection problem
Polynomial-time approximation algorithms
Evaluation based on real traces

11. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Contribution
Formal model for the Data Store Selection problem
Polynomial-time approximation algorithms
Evaluation based on real traces

12. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Contribution
Formal model for the Data Store Selection problem
Polynomial-time approximation algorithms
Evaluation based on real traces

13. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Model
Datastore 1 Datastore 2 Datastore n
C1 C2
. . . Cn
ρ1 ρ2 ρn
β
ρj: probability that x is not in datastore j,
in spite of a positive indication

14. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Model
Datastore 1 Datastore 2 Datastore n
C1 C2
. . . Cn
ρ1 ρ2 ρn
β
ρj: probability that x is not in datastore j,
in spite of a positive indication
Objective: find a subset of datastores D minimizing
The (expected) miss cost, β j∈D ρj , plus ...

15. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Model
Datastore 1 Datastore 2 Datastore n
C1 C2
. . . Cn
ρ1 ρ2 ρn
β
ρj: probability that x is not in datastore j,
in spite of a positive indication
Objective: find a subset of datastores D minimizing
The (expected) miss cost, β j∈D ρj , plus ...
the access cost, j∈D Cj

16. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Model
Datastore 1 Datastore 2 Datastore n
C1 C2
. . . Cn
ρ1 ρ2 ρn
β
ρj: probability that x is not in datastore j,
in spite of a positive indication
The Data Store Selection (DSS) problem:
Find a subset of datastores D which minimizes
φ(D) = β
j∈D
ρj +
j∈D
Cj

17. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Algorithmic Approach
The Data Store Selection (DSS) problem:
Find a subset of datastores D which minimizes
φ(D) = β
j∈D
ρj +
j∈D
Cj

18. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Algorithmic Approach
Assume we know an upper bound on the total access cost
(budget) B
The Data Store Selection (DSS) problem:
Find a subset of datastores D which minimizes
φ(D) = β
j∈D
ρj +
j∈D
Cj

19. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Algorithmic Approach
Assume we know an upper bound on the total access cost
(budget) B
The Data Store Selection (DSS) problem:
Find a subset of datastores D which minimizes
β
j∈D
ρj s.t.
j∈D
Cj ≤ B

20. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Algorithmic Approach
Assume we know an upper bound on the total access cost
(budget) B
The Data Store Selection (DSS) problem:
Find a subset of datastores D which minimizes
j∈D
ρj s.t.
j∈D
Cj ≤ B

21. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Algorithmic Approach
Assume we know an upper bound on the total access cost
(budget) B
Multiplicative optimization → additive optimization
The Data Store Selection (DSS) problem:
Find a subset of datastores D which minimizes
j∈D
log(ρj) s.t.
j∈D
Cj ≤ B

22. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Algorithmic Approach
Assume we know an upper bound on the total access cost
(budget) B
Multiplicative optimization → additive optimization
The Data Store Selection (DSS) problem:
Find a subset of datastores D which maximizes
j∈D
− log(ρj) s.t.
j∈D
Cj ≤ B

23. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Algorithmic Approach
Assume we know an upper bound on the total access cost
(budget) B
Multiplicative optimization → additive optimization
This is effectively a Knapsack problem
The Data Store Selection (DSS) problem:
Find a subset of datastores D which maximizes
j∈D
− log(ρj) s.t.
j∈D
Cj ≤ B

24. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
A Naive approach for the DSS Problem
Run a Knapsack approximation algorithm for budget =
1, . . . , β
For each suggested solution, calculate φ(∗)
Take the arg min of the suggested solutions
The Data Store Selection (DSS) problem:
Find a subset of datastores D which maximizes
j∈D
− log(ρj) s.t.
j∈D
Cj ≤ B

25. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
A Naive approach for the DSS Problem
Run a Knapsack approximation algorithm for budget =
1, . . . , β
For each suggested solution, calculate φ(∗)
Take the arg min of the suggested solutions
This is very costly
Goal: emulate solution space efficiently
The Data Store Selection (DSS) problem:
Find a subset of datastores D which maximizes
j∈D
− log(ρj) s.t.
j∈D
Cj ≤ B

26. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
DSKnap: Using Knap to Solve the DSS Problem
Emulate Knapsack 2-approximation algorithm only once
for every different access cost
The Data Store Selection (DSS) problem:
Find a subset of datastores D which maximizes
j∈D
− log(ρj) s.t.
j∈D
Cj ≤ B

27. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Approximation Algorithms
Alg Approx Run time Based on
DSKnap O
√
β O n2 log n Knap 2-approx
DSPot maxj{Cj} O (n log n) Potential func
DSPP O β 1+ O f (n, ) · min{ j Cj, β}
∗
Knap FPTAS
∗
f (n, ): The run-time of a (1 + )-approx FPTAS

28. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Our Approximation Algorithms
Alg Approx Run time Based on
DSKnap O
√
β O n2 log n Knap 2-approx
DSPot maxj{Cj} O (n log n) Potential func
DSPP O β 1+ O f (n, ) · min{ j Cj, β}
∗
Knap FPTAS
∗
f (n, ): The run-time of a (1 + )-approx FPTAS

29. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Evaluation Settings
Trace: accesses to Wikipedia [UPvS’09]

30. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Evaluation Settings
Trace: accesses to Wikipedia [UPvS’09]
Network layout based on a real-world CDN [OVH]
19 datastores, each storing 1k URLs
False positive ratio: 2%
Access costs based on topology and bandwidth
OVH’s CDN in Europe (www.ovh.co.uk)

31. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Evaluation Settings
Trace: accesses to Wikipedia [UPvS’09]
Network layout based on a real-world CDN [OVH]
19 datastores, each storing 1k URLs
False positive ratio: 2%
Access costs based on topology and bandwidth
19 users, requesting data items, all using either
Cheapest positive indication access policy
All positive indications access policy
DSKnap

32. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Evaluation Settings
Trace: accesses to Wikipedia [UPvS’09]
Network layout based on a real-world CDN [OVH]
19 datastores, each storing 1k URLs
False positive ratio: 2%
Access costs based on topology and bandwidth
19 users, requesting data items, all using either
Cheapest positive indication access policy
All positive indications access policy
DSKnap
A missed item is fetched to either 1, 3, 5 datastores
Compare total access costs, normalized to Opt
equipped with a perfect indicator

33. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Evaluation Results
β Policy 1 location 3 locations 5 locations
102
Cheapest 1.21 1.12 1.09
All 1.09 1.39 1.56
DSKnap 1.10 1.11 1.09
103
Cheapest 1.23 1.11 1.07
All 1.02 1.06 1.08
DSKnap 1.02 1.04 1.03
104
Cheapest 1.24 1.11 1.07
All 1.01 1.02 1.02
DSKnap 1.01 1.02 1.02

34. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Evaluation Results
β Policy 1 location 3 locations 5 locations
102
Cheapest 1.21 1.12 1.09
All 1.09 1.39 1.56
DSKnap 1.10 1.11 1.09
103
Cheapest 1.23 1.11 1.07
All 1.02 1.06 1.08
DSKnap 1.02 1.04 1.03
104
Cheapest 1.24 1.11 1.07
All 1.01 1.02 1.02
DSKnap 1.01 1.02 1.02

35. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Evaluation Results
β Policy 1 location 3 locations 5 locations
102
Cheapest 1.21 1.12 1.09
All 1.09 1.39 1.56
DSKnap 1.10 1.11 1.09
103
Cheapest 1.23 1.11 1.07
All 1.02 1.06 1.08
DSKnap 1.02 1.04 1.03
104
Cheapest 1.24 1.11 1.07
All 1.01 1.02 1.02
DSKnap 1.01 1.02 1.02

36. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Evaluation Results
β Policy 1 location 3 locations 5 locations
102
Cheapest 1.21 1.12 1.09
All 1.09 1.39 1.56
DSKnap 1.10 1.11 1.09
103
Cheapest 1.23 1.11 1.07
All 1.02 1.06 1.08
DSKnap 1.02 1.04 1.03
104
Cheapest 1.24 1.11 1.07
All 1.01 1.02 1.02
DSKnap 1.01 1.02 1.02

37. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Conclusion
Network caching is a fundamental building block in ...
5G networks
Content delivery networks
Wide area networks
Efficient access strategies are important for optimizing
performance
Existing heuristics fall short in versatile scenarios

38. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Conclusion
Network caching is a fundamental building block in ...
5G networks
Content delivery networks
Wide area networks
Efficient access strategies are important for optimizing
performance
Existing heuristics fall short in versatile scenarios
Our algorithm outperforms existing heuristics across the
board

39. Access
Strategies for
Network
Caching
Introduction
Model
Algorithms
Evaluation
Conclusion
Conclusion
Network caching is a fundamental building block in ...
5G networks
Content delivery networks
Wide area networks
Efficient access strategies are important for optimizing
performance
Existing heuristics fall short in versatile scenarios
Our algorithm outperforms existing heuristics across the
board
Questions?
ofanan@gmail.com