Hierarchical Video Caching inWireless Cloud: Approaches and

Algorithms

IEEE ICC 2012 Workshop onRealizing Advanced Video Optimized Wireless Networks

June 15, 2012

Sujit DeyHastii Ahlehagh

UC San Diegodey@ece.ucsd.edu

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Outline

• Motivations• Hierarchical Caching Algorithms• Hierarchical Caching Simulation Results• Video Aware Backhaul Scheduling• Scheduling Simulation Results• Conclusion

2

Motivation for Hierarchical CachingPrevious: Video caching at the RAN to address congestionand improve delay and capacity of video deliveryIssues: 1. High cache miss ratio because of small caches

- Partially addressed by new UPP based caching2. High cache miss ratio due to user mobility

New Developments: Hierarchical caching to address above issues:1. Relatively larger caches at the CN nodes to supplement

RAN caches, while keeping total cache size same2. UPP based caching policies extended to hierarchical caches3. QoE aware Scheduling of RAN backhaul and CN resources

for video fetches from CDN4. Extended simulation framework to include video requests from

multiple cells, and mobility of users between cells

Effects:1. Improved coverage (higher cache hit ratio) than RAN-only caches2. Better address mobility, by increasing likelihood that a video currently being

downloaded is also in CN/RAN cache associated with the new cell during handoff3. Experimental results show significant improvements in cache hit ratio and

capacity, in particular when considering mobility3

What Videos do Users Watch?

• Most Popular Videos (Zipf distribution [1])• National video popularity may not reflect local

video popularity [2], and hence mobile videopreference in an individual cell site, which maydepend on demographics, time of day, etc.

• Strong preference towards some videocategories

SoccerESPN

CNNNews

FoxNews

HuluAuto

SoccerYou

Tube

CNNNews

FoxNews

Football

ESPN

Hulu

SoccerESPN

CNNNews

Hulu

Video Categories

[1] M. Cha et. al., "Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems",IEEE/ACM Transactions on Networking, Vol. 17, No. 5, October 2009

Category Average CumulativeViews – 90 Days

Autos 1501.8

Entertainment 1293.1

Comedy 1267.2

Arts & Animation 1106.3

Animals & Pets 1075.1

Science &Technology 794.6

Sports 745.1

How-To 432.4

Video Games 418.8

Family & Kids 328.1

News & Blogs 302.8

Vlogs 259.1

Travel 157.3

Commercials 124

Reelseo The Online Video Marketing Guide (www.reelseo.com )

[2] Michael Zink, et al.,“Watch Global Cache Local: YouTube Network Traces at a Campus Network - Measurements and Implications.” InProceedings of MMCN 2008, San Jose, CA, USA, Jan 2008

User Preference Profile (UPP): Each user has preference towards specific categories(and sources) of videos watched

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Cell Site Video Preference [1]

0.34

0.3

0.18

0.060.06 0.060.32

0.34

0.34

0.33

0.32

0.26

0.030.03 0.03

Cell UPP Soccer ESPN (SE)Soccer YouTube (SY)CNN News (CN)Fox News (FN)Football ESPN (FE)Hulu (H)

User 1 User 2

Assumption: Users request video with equal probability

UPP of Active Users in the cell:

= ( )| |=1

Likelihood that a video is requested by the Active Users in the cell?

( ) = ( , )| |=1

Most Likely Requested (MLR):

Least Likely Requested (LLR):{ | = 1. . . . ( ) > }{ . . ( ) = }

Given overall MPV, video popularity distribution inVideo Category, vcj: ( , ) = ( )∑ ( )| |=1( ) = ( ) if belongs to category , else ( ) = 0.

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[1] H. Ahlehagh, S, Dey, “Video Caching in Radio Access Network: Impact on Delay and Capacity”,In Proc. IEEE Wireless Communications and Networking Conference, Paris, France, April 2012.

Policies for RAN Micro-Caches [1]• MPV

– Proactively cache the most popular videos from MPV list, subject to cache size;– Cache content of each cell site is identical

• LRU– Reactively cache videos that are associated with cache misses– If cache is full, evict the least recently used video

• P-UPP– If active users change, proactively cache videos from new MLR list– Developed techniques to reduce bandwidth needed to download new MLR

videos, without affecting significantly cache hit ratio• R-UPP

– Reactively cache videos associated with cache misses– If cache is full, use LLR list to select candidate(s) for eviction

• Don’t update cache if the requested video causing the miss is in LLR itself

[1] H. Ahlehagh, S, Dey, “Video Caching in Radio Access Network: Impact on Delay and Capacity”,In Proc. IEEE Wireless Communications and Networking Conference, Paris, France, April 2012.

3G 4G Hierarchical Cache

Model wireless network as a tree where the videos traverse down the treeand AUS Information up the hierarchical tree.

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Hierarchical Caching withinthe Wireless Network

How to distribute the caches most effectively to improve individual cellcoverage and support mobility across cells? Trade off between coverage and mobility

Hierarchical Caching Options1. Inclusive Caching: Suitable for Mobility because

if a user moves from one cell to another, theassociated video that is currently beingdownloaded is guaranteed to be found in the2nd layer cache. 2nd layer cache is limited in size so it may not be able

to replicate all the videos of the RAN caches

2. Exclusive Caching: Improves coverage, as the2nd layer cache stores videos that do not exist inthe 1st layer caches. This leads to overall higherhit ratio. A cache in the 1st layer, e.g. L13 might contain videos

that are more useful for L11 than the rest of thevideos in universe; L11 couldn’t cache the videos dueto the cache size constraints; Not suitable formobility

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L1nL11

L21

Optimal for Mobility

L21=L11U… U L1n

1

L1nL11

L21

Optimal for Coverage

L21=VR(L11) U…VR(L1n) - {L11U… U L1n}

2

3. Our hybrid approach: Each cache makes its cachingdecision independently based on its AUS, e.g. if AUS ofL21 favors VC2 category, L21 caches more videos of thatcategory. To improve coverage, intersection of all the 1st

layer caches are removed from the 2nd layer cache Suitable for mobility and improves coverageL1nL11

L21

Hierarchical UPP based3

= U … − ∩ ∩

VR(L11): videos that L11 would have cached if enough space was available

Policies for Hierarchical Caching• H-MPV

– Each cache in the hierarchy stores videos according to the Most Popular ranking;A higher layer cache excludes the intersection of all the lower layer caches

• H-LRU– Hierarchical LRU is a straight-forward extension of the single-layer LRU and has

built-in exclusivity; No further optimization is required

Extended RAN-only UPP caching policies to higher layer caches In the hierarchical settings, the AUS of the higher layer node is defined as the union

of all AUSs of the lower layer nodes (child nodes) connected to it• H-P-UPP

– Proactively caches videos based on the UPP of the AUS of each cache.Intersection of all the child caches is excluded from the parent cache

• H-R-UPP– Reactively caches each video associated with a miss; while the video is traversing

towards the leaf in the hierarchy tree, each cache on the way to the leaf decideswhether to cache the video based on its AUS

– use LLR list to select candidate(s) for eviction Don’t update cache if the requested video causing the miss is in LLR itself

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10

Video request, V

End

Yes

R-UPP

Layer l = 1

Layer l ++

• Calculate UPP for cache(l)• Calculate request probability, PR , based on

UPP of cache(l)• Calculate MLR and LLR sets• Update the cache if the PR (V) - ∑PR (LLR)

> Threshold)

Is V inCache(l)

?

• Download fromCache(l)

• l --

Yes

NoIsl==0?

Layer l --

No

Yes

AUS Changes in an eNodeB(User arrival or departure)

Morecache

layers?

End

Yes

P-UPP

Layer l = 1

Layer l ++

• Calculate UPP for cache(l)• Calculate request probability, PR , based on UPP of cache(l)• Generate MLR and LLR sets• Update the cache if the PR (MLR) - ∑PR (LLR) > Threshold

(Request probability of the video that need to be added fromMLR is greater than sum probability of videos that need tobe evicted for that video to fit in the cache plus a threshold)

Hierarchical R-UPP and P-UPP Policies

Scheduling Cache Misses• Videos resulting in cache misses have to be fetched from CDNs• Backhaul Bandwidth is a limited/shared resourcemay lead to increase in video delay, and/or impact on capacity

• RAN Backhaul Scheduler: coordinates with video clients, and determinesappropriate rates that can be allocated, so as to– Maximize Video Capacity (number of concurrent video requests that

can be served),– While meeting Video QoE (maximum initial video delay, and ensuring

no stalling during playback).• Relationship between data rate and QoE established by use of

Leaky Bucket Parameters (LBP) [4]: N 3-tuples (R, B, F),R: transmission rate, B: buffer size, F: initial fullness– If data rate is R, and client waits for initial delay of

F/R secs, no stalling during playback.

[4] J. Ribas-Corbera, et al., “A Generalized Hypothetical Reference Decoder for H.264/AVC”, IEEE Transactions on Circuits and Systems, vol. 13, no. 7, July 200311

Hierarchical Backhaul Scheduling• Using LBPs, the video client requests the lowest rate that

satisfies its initial delay requirements,• Successful scheduling depends upon the availability of sufficient

backhaul bandwidth; otherwise the request will be blocked.• After any change in the state of the current backhaul

downloads, recalculate the spare backhaul bandwidth capacityand allocate the spare resource, using the LP formulation below,among the users that have been admitted:Maximize: ∑Subject to: ≥ ∀∑ ∈ ≤ = 1, . . ,

bi: bandwidth of the ith flow, ri: the minimum rate required,Fn: set of flows that go through the nth backhaul

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L2,1

CACHE

L1,1

CACHE

L3,1

CACHE

C2,3

Internet

CDN

L1,2

CACHE

C1,1 C1,2

C3

UkUi…

1 M

bps

Admit the requests according to theirminimum required rate (LBP table)

Redistribute the spare capacity usingLP formulation

(R,B,F) Initial Delay

(193K, 9216, 8146) 42.20 sec

(400K, 9216, 6216) 15.54 sec

(500K, 9216, 6216) 12.42 sec Video’s LBP

Simulation Environment andParameters

• MATLAB based framework to simulate end-to-end video cloud eco-system,including user arrival/departure from a cell, mobility model, UPPs, video requestgeneration, cache policies, backhaul video scheduling

• Mobility is modeled using a Poisson process: users are moved from one cell to aneighboring cell with the mean active cell time of 100seconds

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Variable Distribution/Parameters Value

Total Number of Videos, Video Requests, VideoCategories, Video Popularity Distribution

20,000, 100,000, 250, Zipf 0.8

Video Frame Size Distribution Proposed in [5]

Video Size min=2, mean=8, max=30 minutes

Video Bit Rate 200kbps (QVGA) to 2Mbps (HD)

Total number of mobile users 5,000UPP Distribution Across VCs Exponential, 2User Arrival/Departure Model and VideoRequest Arrival

Poisson:Mean user inter-arrival time = 100 secondsMean user active time = 2700 secondsMean inter-arrival time per user = 120seconds

Backhaul Delay Thresholds [10,20,30]sec,30sec (LP)Cache Size 50Gbyte, 100Gbyte, 150GbyteMobility Model Poisson: mean active cell time = 100sec

Hierarchical Caching

[5] D. M. B. Masi, et al., “Video Frame Size Distribution Analysis”, The Telecommunications Review 2008, Volume 19, Noblis, Falls Church, VA, September 2008

RAN-only Caching

6PGW-only Caching

Simulation Results

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50 100 1500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Cache Size (Gbyte)(a)

CacheHitRatio

MPV

H-MPVLRU

H-LRU

R-UPP

H-R-UPPP-UPP

H-P-UPP

RAN CN Internet0

50

100

150

200

250

300

350

Backhaul Layer(b)

MeanBackhaulBWrequired

MPV

H-MPV

LRU

H-LRUR-UPP

H-R-UPP

P-UPP

H-P-UPP

NoCache MPV LRU R-UPP P-UPP0

100

200

300

400

500

600

700

Cache Policy(c)

Capacity

RAN only CacheHierarchy Cache

NoCache MPV LRU R-UPP P-UPP0

100

200

300

400

500

600

700

800

Cache Policy(d)

Capacity

RAN only Cache w ith mobilityHierarchy Cache w ith mobility

• Hierarchical caching improves cache hit ratio by up to 25 percentage pointcompared to RAN only caching

• Hierarchical caching can improve network capacity by 21% and 30% using H-P-UPP and H-R-UPP compared to RAN only caching

• UPP based hierarchical caching policies perform significantly better in the caseof mobility; hierarchical UPP performs 47% better than RAN-only R-UPP interms of capacity

Cache Size=150Gbyte

RAN CN Internet0

50

100

150

200

250

300

350

Backhaul Layer(b)

MeanBackhaulBWrequired

MPVH-MPVPGW-MPVLRUH-LRUPGW-LRUR-UPPH-R-UPPPGW-R-UPPP-UPPH-P-UPPPGW-P-UPP

50 100 1500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Cache Size (Gbyte)(a)

CacheHitRatio

NoCache MPV LRU R-UPP P-UPP0

100

200

300

400

500

600

700

800

Cache Policy(d)

Capacity

RAN only Cache with mobilityHierarchy Cache with mobilityPGW only Cache with Mobility

NoCache MPV LRU R-UPP P-UPP0

100

200

300

400

500

600

700

800

Cache Policy(c)

Capacity

MPVH-MPVPGW MPVLRUH-LRUPGW-LRUR-UPPH-R-UPPPGW-R-UPPP-UPPH-P-UPPPGW-P-UPP

RAN only CacheHierarchy CachePGW only Cache

Simulation Results

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Cache Size=150Gbyte

• Hierarchical caching improves cache hit ratio by up to 25 percentage pointcompared to RAN-only caching

• Hierarchical caching can improve network capacity by 21% and 30% using H-P-UPP and H-R-UPP compared to RAN only caching

• UPP based hierarchical caching policies perform significantly better in the case ofmobility; hierarchical UPP performs 47% better than RAN-only R-UPP in terms ofcapacity

• PGW-only caching improves cache hit ratio by 8 and 5 percentage point for P-UPP and R-UPP respectively compared to Hierarchical caching. However,improvement in the cache hit ratio comes with the cost of higher RAN and CNrequired backhaul BW compared to RAN-only and Hierarchical caching, andsignificantly lower capacity!

Delay of Scheduled Videos

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Cache Size=150Gbyte

0 5 10 15 20 25 300.4

0.5

0.6

0.7

0.8

0.9

1

Delay (second)

CDFofDelayofScheduledVideos

MPVLRUR-UPPP-UPP

0 5 10 15 20 25 300.4

0.5

0.6

0.7

0.8

0.9

1

Delay (second)

CDFofDelayofScheduledVideos

H-MPVH-LRUH-R-UPPH-P-UPP

0 5 10 15 20 25 300.4

0.5

0.6

0.7

0.8

0.9

1

Delay (second)

CDFofDelayofScheduledVideos

PGW-MPVPGW-LRUPGW-R-UPPPGW-P-UPP

• RAN-only micro-caching performs the best in terms of probability of successfullyscheduled video requests that can meet certain` initial delay compared withHierarchical and PGW-only caching. For example, of the successfully scheduledrequests, the probability of achieving an initial delay of 5 second or less is about0.79 for P-UPP, 0.76 for R-UPP, 0.68 for H-P-UPP, 0.67 for H-R-UPP , and 0.52 forPGW-P-UPP and PGW-R-UPP.

Conclusion

• Proposed hierarchical caching of video contents in the CNto supplement RAN caches

• Showed that hierarchical caching can significantlyimprove cache hit ratio (for same cache size), andimprove capacity in particular under mobility conditions

• Showed that PWG-only caching can significantly improvecache hit ratio (for same cache size), however, it results inhigher required RAN and CN backhaul BW, andsignificantly lower end-to-end capacity because of thebottlenecks in the CN backhaul.

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