In-Network Caching, Information Centric Networking (ICN), Off-Path Caching, Edge Caching, On-Path Caching, Hybrid Caching, Network caching in Future Networks

1. Exploring Off-Path Caching with Edge
Caching in Information Centric Networking*
Anshuman Kalla, Sudhir Sharma
1Anshuman Kalla
* Proc. IEEE International Conference on Computational Techniques in Information and
Communication Technologies (ICCTICT), New Delhi, India, March 11, 2016.
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2. Introduction
• Foundation of current (TCP/IP) networking was laid down in
early 70s when
– Networking resources were scarce
– Multiple-accessing of resources was of prime importance
– This implies years of experience and mature networking facility
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3. Introduction
• Foundation of current (TCP/IP) networking was laid down in
early 70s when
– Networking resources were scarce
– Multiple-accessing of resources was of prime importance
– This implies years of experience and mature networking facility
• Additional support from numerous growth boosters like
– emergence of high speed data communication links,
– refinement in multi-core processors technology,
– exponential and consistent dip in cost of data storage etc.
– flooding of economic hand-held networking devices,
– simultaneous multiple connectivities
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4. Introduction
• Foundation of current (TCP/IP) networking was laid down in
early 70s when
– Networking resources were scarce
– Multiple-accessing of resources was of prime importance
– This implies years of experience and mature networking facility
• Additional support from numerous growth boosters like
– emergence of high speed data communication links,
– refinement in multi-core processors technology,
– exponential and consistent dip in cost of data storage etc.
– flooding of economic hand-held networking devices,
– simultaneous multiple connectivities,
• Thus we expect flawless evolution & facility to be at its best4Anshuman Kalla

5. Introduction
• In spite of years of maturity & technological advancements
– Networking facility falls short of users’ expectations
– The growth seems to retard
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6. Introduction
• In spite of years of maturity & technological advancements
– Networking facility falls short of users’ expectations
– The growth seems to retard
• The issues that have in a way plagued the current TCP/IP
networking are:
– Data Dissemination & Service Accessing (prominent usage)
– Named Host (i.e. no contents due to DNS mapping)
– Mobility (change in IP leads to ongoing applications restart)
– Availability (of content or services preferably close to users)
– Security (absence of data level security)
– Flash Crowd (leads to congestion, DoS, poor QoS etc.)
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7. Introduction
• In spite of years of maturity & technological advancements
– Networking facility falls short of users’ expectations
– The growth seems to retard
• The issues that have in a way plagued the current TCP/IP
networking are:
– Data Dissemination & Service Accessing (prominent usage)
– Named Host (i.e. no contents due to DNS mapping)
– Mobility (change in IP leads to ongoing applications restart)
– Availability (of content or services preferably close to users)
– Security (absence of data level security)
– Flash Crowd (leads to congestion, DoS, poor QoS etc.)
• Trend is to deploy dedicated fix for every issue encountered7Anshuman Kalla

8. The Facts
• First Fact: Increasing add-on patches for various issues
– Has transformed TCP/IP into complex and delicate architecture
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9. The Facts
• First Fact: Increasing add-on patches for various issues
– Has transformed TCP/IP into complex and delicate architecture
• Second Fact: Today resources are no more limited
– In fact more number of networking enabled devices per person
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10. The Facts
• First Fact: Increasing add-on patches for various issues
– Has transformed TCP/IP into complex and delicate architecture
• Second Fact: Today resources are no more limited
– In fact more number of networking enabled devices per person
• Third Fact: Shift in primary usage of networking facility
– instead of sharing of network resources the prime usage is content
centric
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11. The Facts
• First Fact: Increasing add-on patches for various issues
– Has transformed TCP/IP into complex and delicate architecture
• Second Fact: Today resources are no more limited
– In fact more number of networking enabled devices per person
• Third Fact: Shift in primary usage of networking facility
– instead of sharing of network resources the prime usage is content
centric
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Thus radical change in its usage is the crux of various issues

12. Information Centric Networking
• Lately researchers have felt the need of clean-slate
approach
– To reconcile all the issues and shift in usage in a unified manner
– This marks the birth of Information Centric Networking (ICN)
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13. Information Centric Networking
• Lately researchers have felt the need of clean-slate
approach
– To reconcile all the issues and shift in usage in a unified manner
– This marks the birth of Information Centric Networking (ICN)
• Various proposals are CCN, PSIRP, DONA, PURSUIT etc.
• Albeit design details are different nevertheless all aim
– to retire host-centric & bring in place content-centric model
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14. Information Centric Networking
• Lately researchers have felt the need of clean-slate
approach
– To reconcile all the issues and shift in usage in a unified manner
– This marks the birth of Information Centric Networking (ICN)
• Various proposals are CCN, PSIRP, DONA, PURSUIT etc.
• Albeit design details are different nevertheless all aim
– to retire host-centric & bring in place content-centric model
• Content Centric Networking (CCN) has received significant
popularity
– Thus for present work CCN and its related terminology has been
used.
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15. Salient Features of ICN
• Named content
• In-network caching
• Named based routing
• Data-level security
• Multi-path routing
• Hop-by-hop flow control
• Pull-based communication
• Adaptability to Multiple simultaneous connectivities
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16. Salient Features of ICN
• Named content
• In-network caching  secondary point-of-service
• Named based routing
• Data-level security
• Multi-path routing
• Hop-by-hop flow control
• Pull-based communication
• Adaptability to Multiple simultaneous connectivities
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17. Types of In-Network Caching
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In-Network Caching in ICN
Off-Path Caching Edge CachingOn-Path Caching
Hybrid Caching

18. Types of In-Network Caching
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• On-Path Caching
• Off-Path Caching
• Edge Caching
R1
R2
R3R4
R5R8
R7 R6
Interest Packet

19. Types of In-Network Caching
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Interest Packet
Data Packet
R1
R2
R3R4
R5R8
R7 R6
Nodes that could
cache data are R1 R2
R3 and R6
• On-Path Caching
• Off-Path Caching
• Edge Caching

20. Types of In-Network Caching
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• On-Path Caching
• Off-Path Caching
• Edge Caching
Interest Packet
R1
R2
R3R4
R5R8
R7 R6
Node R4 is
designated
off-path cache

21. Types of In-Network Caching
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• On-Path Caching
• Off-Path Caching
• Edge Caching
Interest Packet
R1
R2
R3R4
R5R8
R7 R6
Data Packet
Node R4 is
designated
off-path cache

22. Types of In-Network Caching
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• On-Path Caching
• Off-Path Caching
• Edge Caching
Interest Packet
R1
R2
R3R4
R5R8
R7 R6

23. Types of In-Network Caching
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• On-Path Caching
• Off-Path Caching
• Edge Caching
Interest Packet
Data Packet
R1
R2
R3R4
R5R8
R7 R6
Node R6 is
edge cache

24. Aim - First
• To empirically compare the performance of on-path,
off-path and edge caching [All Three]
– Researchers already compared performance of on-path and
edge caching techniques
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25. Aim - First
• To empirically compare the performance of on-path,
off-path and edge caching [All Three]
– Researchers already compared performance of on-path and
edge caching techniques
• If marginal performance gap is affordable then edge caching is better
as it involves only edge nodes (Ref this paper for references)
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26. Aim - First
• To empirically compare the performance of on-path,
off-path and edge caching [All Three]
– Researchers already compared performance of on-path and
edge caching techniques
• If marginal performance gap is affordable then edge caching is better
as it involves only edge nodes (Ref this paper for references)
– However comparison of three would answer the questions
• Which one of the three caching technique performs the best?
• Is pervasive caching (i.e. caching at all nodes) really beneficial?
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27. Performance Metrics Used
• Hit Ratio
– Indicates availability of contents
– Need to be maximized
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28. Performance Metrics Used
• Hit Ratio
– Indicates availability of contents
– Need to be maximized
• Average Retrieval Delay
– Smaller the metric better is QoS perceived by users
– Need to be minimized
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29. Performance Metrics Used
• Hit Ratio
– Indicates availability of contents
– Need to be maximized
• Average Retrieval Delay
– Smaller the metric better is QoS perceived by users
– Need to be minimized
• Unique Contents Cached
– Implies cache diversity
– Need to be maximized
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30. Performance Metrics Used
• Hit Ratio
– Indicates availability of contents
– Need to be maximized
• Average Retrieval Delay
– Smaller the metric better is QoS perceived by users
– Need to be minimized
• Unique Contents Cached
– Implies cache diversity
– Need to be maximized
• Percentage of External Traffic
– Signifies use of expensive external links and load on server
– Need to be minimized 30Anshuman Kalla

31. Environment Set-up & Parameters Used
• Six real network topologies were considered:
– Abilene (12 Core nodes), Geant (22), Germany50 (50), India35 (35),
Exodus US (79) & Ebone Europe (87)
• Number of server – One
• Randomly nodes connected to server – 7% to 8%
• Randomly nodes connected to clients – 50% to 55%
• Size of content population – 1000 * number of core nodes
• Cache size per node – 100
• Network cache budget – 10% of content population
• Popularity distribution – Zipfian (α = 0.8)
• Distance from edge nodes to server – 100 ms
• Content Size – homogeneous (unit size)
• Network Regime – Congestion free
• Replacement policy – LRU
• Forwarding over shortest path based on link latency
• Total number of requests simulated – 5,00,000 31

32. Result of Performance Evaluation
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• Six different network topologies and three different
caching techniques results in
– eighteen different scenarios

33. Result of Performance Evaluation
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• Six different network topologies and three different
caching techniques results in
– eighteen different scenarios
• Ten simulations per scenario and results depicts mean
values with standard deviation

34. Result of Performance Evaluation
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• Six different network topologies and three different
caching techniques results in
– eighteen different scenarios
• Ten simulations per scenario and results depicts mean
values with standard deviation
• Overall values of hit ratio or average retrieval delay is
computed by considering all the requests concerning
all the contents

35. Result of Performance Evaluation
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• Six different network topologies and three different
caching techniques results in
– eighteen different scenarios
• Ten simulations per scenario and results depicts mean
values with standard deviation
• Overall values of hit ratio or average retrieval delay is
computed by considering all the requests concerning
all the contents

36. Result of Performance Evaluation
36Anshuman Kalla
• Six different network topologies and three different
caching techniques results in
– eighteen different scenarios
• Ten simulations per scenario and results depicts mean
values with standard deviation
• Overall values of hit ratio or average retrieval delay is
computed by considering all the requests concerning
all the contents

37. Result of Performance Evaluation
37Anshuman Kalla
• Six different network topologies and three different
caching techniques results in
– eighteen different scenarios
• Ten simulations per scenario and results depicts mean
values with standard deviation
• Overall values of hit ratio or average retrieval delay is
computed by considering all the requests concerning
all the contents

38. Result of Performance Evaluation
38Anshuman Kalla
• Six different network topologies and three different
caching techniques results in
– eighteen different scenarios
• Ten simulations per scenario and results depicts mean
values with standard deviation
• Overall values of hit ratio or average retrieval delay is
computed by considering all the requests concerning
all the contents

39. Result of Performance Evaluation
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• Six different network topologies and three different
caching techniques results in
– eighteen different scenarios
• Ten simulations per scenario and results depicts mean
values with standard deviation
• Overall values of hit ratio or average retrieval delay is
computed by considering all the requests concerning
all the contents
Though edge caching performs better than on-
path caching, however off-path caching
performs the best.

40. Cumulative External Traffic (Exodus US)
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41. Content-Wise Hit Ratio (Exodus US)
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42. Content-Wise Average Retrieval Delay
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43. Result of Performance Evaluation
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44. Off-Path Caching performs the best
as compared to On-Path and Edge Caching
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Conclusion & Motivation

45. Off-Path Caching performs the best
as compared to On-Path and Edge Caching
However lets review the
content-wise average retrieval delay plot
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Conclusion & Motivation

46. Conclusion & Motivation
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Content-Wise Average Retrieval Delay

47. Conclusion & Motivation
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Content-Wise Average Retrieval Delay
Lets zoom this section

48. 48Anshuman Kalla
Conclusion & Motivation
Content-Wise Average Retrieval Delay

49. 49Anshuman Kalla
Conclusion & Motivation
Content-Wise Average Retrieval Delay
Note the gap in terms of delay
for top most popular contents

50. 50Anshuman Kalla
Problem Targeted
Content-Wise Average Retrieval Delay
Is it possible to devise a caching technique that
– could achieve minimum content-wise average retrieval delay for top
most popular contents like edge caching while
– maintaining overall performance very close to that of off-path caching

51. Aim - Second
• To couple off-path with edge caching  hybrid
 That could reduce average retrieval delay for the top most
popular contents while
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52. Aim - Second
• To couple off-path with edge caching  hybrid
 That could reduce average retrieval delay for the top most
popular contents while
 Marginally scarifying other relevant performance metrics
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We propose Hybrid Caching  Coupling Off-Path Caching with Edge Caching

53. EDOP (EDge Off-Path) Caching
• Simple coupling results in two devitalizing issues
– Reduction in cache diversity due to content duplication
– Blind (edge) caching at boundary nodes  hog the limited space
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54. EDOP (EDge Off-Path) Caching
• Simple coupling results in two devitalizing issues
– Reduction in cache diversity due to content duplication
– Blind (edge) caching at boundary nodes  hog the limited space
• Flavor of edge caching is being introduced to off-path
caching
– Caches at the edge nodes are partitioned
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55. Anshuman Kalla 55
EDOP (EDge Off-Path) Caching
R1
R2
R3R4
R5R8
R7 R6
Partitioned of Content Store
at edge nodes

56. EDOP (EDge Off-Path) Caching
• Simple coupling results in two devitalizing issues
– Reduction in cache diversity due to content duplication
– Blind (edge) caching at boundary nodes  hog the limited space
• Flavor of edge caching is being introduced to off-path
caching
– Caches at the edge nodes are partitioned
– Tuning parameter T  percentage of storage for edge caching
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57. Anshuman Kalla 57
EDOP (EDge Off-Path) Caching
R1
R2
R3R4
R5R8
R7 R6
Partitioned of CS at edge nodes
using Tuning Parameter ‘T’

58. EDOP (EDge Off-Path) Caching
• Simple coupling results in two devitalizing issues
– Reduction in cache diversity due to content duplication
– Blind (edge) caching at boundary nodes  hog the limited space
• Flavor of edge caching is being introduced to off-path
caching
– Caches at the edge nodes are partitioned
– Tuning parameter T  percentage of storage for edge caching
• Content selection to be made before edge caching
– FIFO queue for reference counting i.e. popularity estimation
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59. Anshuman Kalla 59
EDOP (EDge Off-Path) Caching
R1
R2
R3R4
R5R8
R7 R6
Partitioned of CS at edge nodes

60. EDOP (EDge Off-Path) Caching
• Simple coupling results in two devitalizing issues
– Reduction in cache diversity due to content duplication
– Blind (edge) caching at boundary nodes  hog the limited space
• Flavor of edge caching is being introduced to off-path
caching
– Caches at the edge nodes are partitioned
– Tuning parameter T  percentage of storage for edge caching
• Content selection to be made before edge caching
– FIFO queue for reference counting i.e. popularity estimation
• Pre-fetching of popular contents estimated by FIFO
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61. Results and Discussion
Caching Hit Ratio Average Retrieval Delay Unique Cached Content
On-Path 0.0944 (±0.0003) 100.7412 (±0.0352) 2527 (±13)
Edge 0.1027 (±0.0003) 99.6784 (±0.0267) 5810 (±12)
Off-Path 0.4637 (±0.0001) 84.4653 (±0.1751) 7900 (±0)
EDOP 0.4545 (±0.0003) 84.0432 (±0.1914) 7465 (±2)
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62. Results and Discussion
Caching Hit Ratio Average Retrieval Delay Unique Cached Content
On-Path 0.0944 (±0.0003) 100.7412 (±0.0352) 2527 (±13)
Edge 0.1027 (±0.0003) 99.6784 (±0.0267) 5810 (±12)
Off-Path 0.4637 (±0.0001) 84.4653 (±0.1751) 7900 (±0)
EDOP 0.4545 (±0.0003) 84.0432 (±0.1914) 7465 (±2)
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63. Results and Discussion
Caching Hit Ratio Average Retrieval Delay Unique Cached Content
On-Path 0.0944 (±0.0003) 100.7412 (±0.0352) 2527 (±13)
Edge 0.1027 (±0.0003) 99.6784 (±0.0267) 5810 (±12)
Off-Path 0.4637 (±0.0001) 84.4653 (±0.1751) 7900 (±0)
EDOP 0.4545 (±0.0003) 84.0432 (±0.1914) 7465 (±2)
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<1% <6%

64. Results and Discussion
Caching Hit Ratio Average Retrieval Delay Unique Cached Content
On-Path 0.0944 (±0.0003) 100.7412 (±0.0352) 2527 (±13)
Edge 0.1027 (±0.0003) 99.6784 (±0.0267) 5810 (±12)
Off-Path 0.4637 (±0.0001) 84.4653 (±0.1751) 7900 (±0)
EDOP 0.4545 (±0.0003) 84.0432 (±0.1914) 7465 (±2)
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<1% <6%
•The gain achieved in content-
wise average retrieval delay is
between 88% to 3% for the
top most popular contents
•At the cost of max 6%
deterioration in other relevant
parameters

65. Results and Discussion
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66. Conclusion and Future Scope
• The two fold contribution of the paper is as follow:
– Empirically, it has been proven that off-path caching
outperforms the on-path and edge caching techniques
– Hybrid caching like EDOP caching has potential to
improve performance of in-network caching
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67. Conclusion and Future Scope
• The two fold contribution of the paper is as follow:
– Empirically, it has been proven that off-path caching
outperforms the on-path and edge caching techniques
– Hybrid caching like EDOP caching has potential to
improve performance of in-network caching
• Issues that will be targeted in future are:
– What should be the optimum value of T and how it
should be determined?
– How to ensure that edge caches retain the most
popular contents?
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68. Thank You
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