MuNCC: Multi-hop Neighborhood Collaborative Caching...

MuNCC:Multi-hop Neighborhood Collaborative

Caching in Information-Centric Networks

Travis Mick, Reza Tourani, Satyajayant MisraNew Mexico State UniversityDept. of Computer Science

28 Sept. 2016ACM ICN, Kyoto

Travis Mick, Reza Tourani, Satyajayant Misra MuNCC: Multi-hop Neighborhood Collaborative Caching

1

Outline

Introduction

Prior Work

Solution Design

Experimental Results

Conclusion & Future Work


2

The amount of cacheable content is growing...

VideoTraffic

63% ofall traffic

79% oftraffic by

2020

1.8 ZBper year

2016 Cisco VNITravis Mick, Reza Tourani, Satyajayant Misra MuNCC: Multi-hop Neighborhood Collaborative Caching

3

The ideal caching scheme has several requirements.

IdealCaching

HighHit Ratio

LowOverhead

LowLatency

ReducedCost

ImprovedQoE


4

Caching encompasses several tightly-coupled problems.

AdmissionControl

ReplicaPlacement

Routing& Forwarding

StateExchange

RedundancyElimination


4

Outline

Introduction

Prior Work

Solution Design




5

Caching strategies can be broadly classified into fourcategories, each with their own shortcomings.

IndependentCaching

On-PathCoordination

NeighborhoodCoordination

GlobalCoordination


5

Caching strategies can be broadly classified into fourcategories, each with their own shortcomings.

IndependentCaching

On-PathCoordination

NeighborhoodCoordination

GlobalCoordination

HighRedundancy

LimitedDiversity ? High

Overhead


6

On-path caching has minimal overhead.

ContentProvider

Consumer

On-pathReplica


7

On-path caching has minimal overhead; low diversity.

ContentProvider

Off-pathReplica

Consumer


7

On-path caching has minimal overhead; low diversity.

ContentProvider

Off-pathReplica

Consumer

NewReplica


8

Attempts have been made to improve on-path diversity.

Leave Copy Everywhere (LCE)Leave Copy Down (LCD), Move Copy Down (MCD)1.ProbCache2, Prob-PD3.

1Laoutaris, N., Che, H., & Stavrakakis, I. (2006).2Psaras, I., Chai, W. K., & Pavlou, G. (2012).3Ioannou, A., & Weber, S. (2014).


9

Hash routing maximizes diversity.

ContentProvider

DesignatedReplica

Consumer


9

Hash routing maximizes diversity; increases path stretch.

ContentProvider

DesignatedReplica

Consumer

ContentProvider

DesignatedReplica

Consumer


10

Variants of hash routing aim to reduce path stretch.

ContentProvider

DesignatedReplica

Consumer

Asymmetric

ContentProvider

DesignatedReplica

Consumer

Multicast

Saino, L., Psaras, I., & Pavlou, G. (2013).Travis Mick, Reza Tourani, Satyajayant Misra MuNCC: Multi-hop Neighborhood Collaborative Caching

11

The neighborhood can contain diverse content.

SearchingNode

ImmediateNeighbors

2nd-hopNeighbors

3rd-hopNeighbors

DistantContentProvider

NearbyReplica


12

Neighborhood caching in ICN is not well-studied.

Caching schemes utilizing Bloom filters.Radius Validation Applicability

Tortelli, M., et al. 1-hop None CCNWang, Y., et al. Arbitrary Theoretical General ICNLee, M., et al. Arbitrary Simulation IP

Wong, W., et al. Arbitrary Emulation CCN


12

Outline

Introduction

Prior Work

Solution Design




13

MuNCC consists of four major components.

Cache StateExchange

NeighborhoodForwarding

ErrorReduction

DiversityEnhancement


14

Cached contents are advertised in Bloom filters.

Attenuated Bloom Filter Construction:1 Compress cache state into a Bloom filter.2 Send Bloom filter to neighbors.3 Aggregate received Bloom filters.4 Repeat h times to establish h-hop neighborhood.


15

Cache summary exchange for BF construction: Step 1.

v

w x

y z

Bv

Bw Bx

By Bz

1 Compress cache state into a Bloom filter.


15


v

w x

y z

Bv

Bv

Bw

Bw

Bx

BxBy

Bz

2 Send Bloom filter to neighbors.


15


v

w x

y z

Bv(w ,0) = BwBv

(x ,0) = Bx

Bw(v ,0) = BvBw

(y ,0) = By

Bx(v ,0) = BvBx

(z,0) = Bz

By(w ,0) = Bw Bz

(x ,0) = Bx

3 Aggregate received Bloom filters.


15


v

w x

y z

Bw ∪ Bx

Bw ∪ BxBv ∪ By

Bv ∪ By

Bv ∪ Bz

Bv ∪ BzBw

Bx

4 Repeat.


15


v

w x

y z

Bv(w ,1) = Bv ∪ ByBv

(x ,1) = Bv ∪ Bz

Bw(v ,1) = Bw ∪ BxBw

(y ,1) = Bw

Bx(v ,1) = Bw ∪ BxBx

(z,1) = Bx

By(w ,1) = Bv ∪ By Bz

(x ,1) = Bv ∪ Bz

4 Repeat.


16

Forwarding decisions are made based on attenuated BFs.

Implementation:Add distance flag D to interest, with default ∞.If D 6=∞, the interest is part of a neighborhood search.

If D =∞:1 Try to satisfy from cache.2 Try to satisfy in neighborhood.3 Forward toward content provider.

If D 6=∞:1 Try to satisfy in level D of neighborhood.2 Return NACK.


17

Neighborhood search procedure: Scenario.

v

w x

y z

SearchingNode

TruePositive

FalsePositive

v : Wants content i .v : i 6∈ local cache


17

Neighborhood search procedure: Level 1.

v

w x

y z

SearchingNode

TruePositive

FalsePositive

v : i 6∈ Bv(w ,0)

v : i ∈ Bv(x,0)


17

Neighborhood search procedure: False positive.

v

w x

y z

v : Forward to x with tag D = 0x : i 6∈ local cachex : Return NACK to v


17

Neighborhood search procedure: Level 2.

v

w x

y z

v : Process NACK from xv : i ∈ Bv

(w,1)v : Forward to w with tag D = 1


17

Neighborhood search procedure: Next hop, level 1.

v

w x

y z

w : i ∈ Bw(y,0)

w : Forward to y with tag D = 0


17

Neighborhood search procedure: Content delivery.

v

w x

y z

y : i ∈ local cachey : Deliver content to ww : Deliver content to v


18

Cache churn undermines BF usefulness and is detrimentalto performance.

Eviction FalsePositive NACKs

IncreasedLatency


19

Several mechanisms reduce the impact of cache churn.

HandlingChurn

GuaranteeingAdvertised

Content

PreventingRecurrent

Error

AdvancedReplacement

Strategy

Two-LevelCache

NegativeBloom Filter

NACKResponse


20

Diversity is enhanced both implicitly and explicitly.

NeighborhoodAwareness

Don’t CacheIf Cached

Nearby

ImplicitDiversification

Evict IfCached

Next Door

ExplicitDiversification


20

Outline

Introduction

Prior Work

Solution Design




21

Experiment SetupSimulation Engine: Icarus 4.

Topologies: Real: WIDE (30, 33), SPRINT (604, 2268),(|V |, |E |) TISCALI (240, 810), GEANT (53, 61) 5.

Scale-free: Topo1 (250, 492), Topo2 (500, 972),Topo3 (750, 1447), Topo4 (1000, 1897).

Workloads: Stationary (Zipf, α = 0.8, N = 3 · 106),Temporal (Globetraff 6, N ≈ 2.1 · 105)

Interests Simulated: 1, 200, 000 per scenario

Cache Sizes: Equivalent to {0.25%, 0.5%, 2%, 5%}of content globally, uniformly distributed.

Parameters: Exchange every 3 minutes, BF size < 4KB, LFU.

Comparison: HR Symmetric, HR Hybrid AM, ProbCache, LCD.4Saino, L., Psaras, I., & Pavlou, G. (2014).5Spring, N., Mahajan, R., Wetherall, D. (2002).6Katsaros, K., Xylomenos, G., & Polyzos, G. (2012).


22

Cache hit ratio: temporal model, real graphs.Higher hit ratio than on-path; exceeds HR in some scenarios.

0.00 0.02 0.04 0.06 0.08 0.10

Cache to population ratio

16

18

20

22

24

26

28

30

Late

ncy (

ms)

MuNCC/4/deg- HR Symm HR Hybrid AM ProbCache LCD

0.00 0.02 0.04 0.06 0.08 0.10


0.05

0.10

0.15

0.20

0.25

0.30

Ca

ch

e h

it r

atio

GEANT (53, 61)

0.00 0.02 0.04 0.06 0.08 0.10


0.00

0.05

0.10

0.15

0.20

0.25

0.30

Ca

ch

e h

it r

atio

TISCALI (240, 810)

0.00 0.02 0.04 0.06 0.08 0.10


0.05

0.10

0.15

0.20

0.25

0.30

Ca

ch

e h

it r

atio

WIDE (30, 33)

0.00 0.02 0.04 0.06 0.08 0.10


0.00

0.05

0.10

0.15

0.20

0.25

0.30

Ca

ch

e h

it r

atio

SPRINT (604, 2268)


23

Latency: temporal model, real graphs.Latency very close to LCD, often lower.

0.00 0.02 0.04 0.06 0.08 0.10


16

18

20

22

24

26

28

30

Late

ncy (

ms)


0.00 0.02 0.04 0.06 0.08 0.10


20

22

24

26

28

30

32

La

ten

cy (

ms)

GEANT (53, 61)

0.00 0.02 0.04 0.06 0.08 0.10


25

30

35

40

45

50

La

ten

cy (

ms)

TISCALI (240, 810)

0.00 0.02 0.04 0.06 0.08 0.10


12

14

16

18

20

22

24

La

ten

cy (

ms)

WIDE (30, 33)

0.00 0.02 0.04 0.06 0.08 0.10


18

20

22

24

26

28

30

32

34

La

ten

cy (

ms)

SPRINT (604, 2268)


24

Overhead: temporal model, real graphs.

False Positive Rate Overhead Rate0.5% 0.001372 0.000497

WIDE 2% 0.000819 0.0009495% 0.000668 0.001170

0.5% 0.003139 0.000725GEANT 2% 0.002315 0.001250

5% 0.002120 0.0015270.5% 0.004133 0.000415

TISCALI 2% 0.004948 0.0008095% 0.004093 0.001139

0.5% 0.006620 0.001422SPRINT 2% 0.006979 0.002333

5% 0.004843 0.002962

FP rate: Probability that an interest experiences at least one FP.Overhead rate: BF transmissions per second by each node.


24

Outline

Introduction

Prior Work

Solution Design




25

MuNCC shows feasibility for neighborhood collaboration.

LowLatency

LowOverhead

HighHit Ratio

On-Path

HashRouting

GlobalCollaboration


25

MuNCC shows feasibility for neighborhood collaboration.

LowLatency

LowOverhead

HighHit Ratio

x

MuNCC


26

Future work aims to improve MuNCC’s performance.

False Positive MitigationDynamic BF sizing.Proactive eviction notification.Negative BF aggregation.

Redundancy EliminationMulti-hop consolidation.Computational overhead reduction.


26

Thank you!

[email protected]

Funded by US National Science Foundation and US Dept. of Defense.Travis Mick, Reza Tourani, Satyajayant Misra MuNCC: Multi-hop Neighborhood Collaborative Caching

27

Any on-path node can initiate an expanding-ring search inthe neighborhood.

if D =∞ and content not in cache thenfor each level of the neighborhood, D′ ∈ [1, h − 1] do

for each neighbor p, sorted by rank doif content in BA

(p, D′−1) thenforward to p with D = D′ − 1if interest satisfied then

breakelse

forward toward content provider with D =∞


28

The interest can indicate the exact cache level to index,reducing compute burden and latency at the next hop.

if D 6=∞ thenif D = 0 then

check local cacheif content not found then

send NACKelse

for each neighbor p doif content in BA

(p, D′−1) thenforward to p with D = D′ − 1if interest satisfied then

breakelse

send NACK


29

Cache hit ratio: stationary model, real graphs.

0.00 0.02 0.04 0.06 0.08 0.10


16

18

20

22

24

26

28

30

Late

ncy (

ms)


0.00 0.02 0.04 0.06 0.08 0.10


0.2

0.3

0.4

0.5

0.6C

ach

e h

it r

atio

GEANT (53, 61)

0.00 0.02 0.04 0.06 0.08 0.10


0.1

0.2

0.3

0.4

0.5

0.6

Ca

ch

e h

it r

atio

TISCALI (240, 810)

0.00 0.02 0.04 0.06 0.08 0.10


0.2

0.3

0.4

0.5

0.6

Ca

ch

e h

it r

atio

WIDE (30, 33)

0.00 0.02 0.04 0.06 0.08 0.10


0.1

0.2

0.3

0.4

0.5

0.6

Ca

ch

e h

it r

atio

SPRINT (604, 2268)


30

Latency: stationary model, real graphs.

0.00 0.02 0.04 0.06 0.08 0.10


16

18

20

22

24

26

28

30

Late

ncy (

ms)


0.00 0.02 0.04 0.06 0.08 0.10


16

18

20

22

24

26

28

30L

ate

ncy (

ms)

GEANT (53, 61)

0.00 0.02 0.04 0.06 0.08 0.10


20

25

30

35

40

45

La

ten

cy (

ms)

TISCALI (240, 810)

0.00 0.02 0.04 0.06 0.08 0.10


10

12

14

16

18

20

22

24

La

ten

cy (

ms)

WIDE (30, 33)

0.00 0.02 0.04 0.06 0.08 0.10


16

18

20

22

24

26

28

30

32

La

ten

cy (

ms)

SPRINT (604, 2268)


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Cache hit ratio: temporal model, scale-free graphs.

0.00 0.02 0.04 0.06 0.08 0.10


16

18

20

22

24

26

28

30

Late

ncy (

ms)


0.00 0.02 0.04 0.06 0.08 0.10


0.00

0.05

0.10

0.15

0.20

0.25

0.30C

ach

e h

it r

atio

Topo1 (250, 492)

0.00 0.02 0.04 0.06 0.08 0.10


0.00

0.05

0.10

0.15

0.20

0.25

0.30

Ca

ch

e h

it r

atio

Topo2 (500, 972)

0.00 0.02 0.04 0.06 0.08 0.10


0.00

0.05

0.10

0.15

0.20

0.25

0.30

Ca

ch

e h

it r

atio

Topo3 (750, 1447)

0.00 0.02 0.04 0.06 0.08 0.10


0.00

0.05

0.10

0.15

0.20

0.25

0.30

Ca

ch

e h

it r

atio

Topo4 (1000, 1897)


32

Latency: temporal model, scale-free graphs.

0.00 0.02 0.04 0.06 0.08 0.10


16

18

20

22

24

26

28

30

Late

ncy (

ms)


0.00 0.02 0.04 0.06 0.08 0.10


20

22

24

26

28

30

32

34

36

38L

ate

ncy (

ms)

Topo1 (250, 492)

0.00 0.02 0.04 0.06 0.08 0.10


25

30

35

40

45

50

La

ten

cy (

ms)

Topo2 (500, 972)

0.00 0.02 0.04 0.06 0.08 0.10


30

35

40

45

50

55

60

La

ten

cy (

ms)

Topo3 (750, 1447)

0.00 0.02 0.04 0.06 0.08 0.10


30

35

40

45

50

55

60

La

ten

cy (

ms)

Topo4 (1000, 1897)


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