Exploring Off-Path Caching with Edge Caching in Information Centric Networking*

Anshuman Kalla, Sudhir Sharma

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* Proc. IEEE International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), New Delhi, India, March 11, 2016.

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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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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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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 best 4Anshuman Kalla

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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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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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

The Facts

• First Fact: Increasing add-on patches for various issues

– Has transformed TCP/IP into complex and delicate architecture

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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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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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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

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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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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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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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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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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Types of In-Network Caching

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In-Network Caching in ICN

Off-Path Caching Edge CachingOn-Path Caching

Hybrid Caching

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

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

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 designatedoff-path cache

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 designatedoff-path cache

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

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

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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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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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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Performance Metrics Used

• Hit Ratio– Indicates availability of contents

– Need to be maximized

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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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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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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

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

Result of Performance Evaluation

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• Six different network topologies and three different caching techniques results in

– eighteen different scenarios

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 meanvalues with standard deviation

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 meanvalues with standard deviation

• Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents

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 meanvalues with standard deviation

• Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents

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 meanvalues with standard deviation

• Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents

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 meanvalues with standard deviation

• Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents

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 meanvalues with standard deviation

• Overall values of hit ratio or average retrieval delay is computed by considering all the requests concerning all the contents

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 meanvalues 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.

Cumulative External Traffic (Exodus US)

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Content-Wise Hit Ratio (Exodus US)

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Content-Wise Average Retrieval Delay

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Result of Performance Evaluation

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Off-Path Caching performs the bestas compared to On-Path and Edge Caching

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Conclusion & Motivation

Off-Path Caching performs the bestas compared to On-Path and Edge Caching

However lets review the content-wise average retrieval delay plot

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Conclusion & Motivation

Conclusion & Motivation

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Content-Wise Average Retrieval Delay

Conclusion & Motivation

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Content-Wise Average Retrieval Delay

Lets zoom this section

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Conclusion & Motivation

Content-Wise Average Retrieval Delay

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Conclusion & Motivation

Content-Wise Average Retrieval Delay

Note the gap in terms of delay for top most popular contents

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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

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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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

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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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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EDOP (EDge Off-Path) Caching

R1

R2

R3R4

R5R8

R7 R6 Partitioned of Content Store at edge nodes

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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EDOP (EDge Off-Path) Caching

R1

R2

R3R4

R5R8

R7 R6

Partitioned of CS at edge nodesusing Tuning Parameter ‘T’

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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EDOP (EDge Off-Path) Caching

R1

R2

R3R4

R5R8

R7 R6

Partitioned of CS at edge nodes

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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Results and DiscussionCaching 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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Results and DiscussionCaching 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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Results and DiscussionCaching 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%

Results and DiscussionCaching 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

Results and Discussion

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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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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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Thank You

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