Wireless Caching: learning time- varying popularity

Transcription

1 Wireless Caching: learning time- varying popularity Georgios Paschos Joint work with M. Leconte, L. Gkatzikis, M. Draief, S. Vassilaras, S. Chouvardas Huawei Technologies, Paris Tyrrhenian workshop - Livorno

2 The mobile traffic tsunami Traffic volume per year 10x Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, White Paper

3 Wireless caching Can we apply web caching to mobile networks? Operator CDN Elayoubi- Roberts, Performance and Cost Effectiveness of Caching in Mobile Access Networks, 2016 Base station Leconte- Paschos- Gkatzikis- Draief- Vassilaras- Chouvardas, Placing Dynamic Content in caches with small population, 2016 Femto/pico User - level Main challenge we will focus: Time- varying popularity - small user populations P- Bastug- Land- Caire- Debbah, Wireless Caching, Technical Misconceptions and Business Barriers, 2016

4 How caching mitigates traffic? Requests served from server Requests served from cache Reduced traffic No caching Caching

5 Performance Analysis for CDN Hit: a request for a cached content Miss: a request for content that exists at the origin server network network cache Hit probability determines cache performance

6 Basic ingredients requests Catalogue M cached contents C Normalized cache size γ=c/m Request sequence high intensity (popular) low intensity (unpopular) aggregate request sequence Hit probability maximization Cache the most popular

7 IRM model for request sequence Poisson of rate λ, independent requests for m with rate λ m =λp m p m is the probability to request m (popularity) Popularities are power law popularity order File popularity p m (log) s: power law exponent Inclination in the log- log plot Crucial for the efficiency of caching 10 files ordered (log)

8 Power law is good news Caching a fraction of the catalog = truncating [p m ] at C Power- law is more skewed than uniform The popular files are requested again and again => many hits However, getting to very high hit rates becomes progressively very difficult popularity probability Summed for hit probability Summed for miss probability Cache size C Entire file catalogue M

9 Caching likes skewed distributions 1.0 Zipf(0.95) Hit probability Zipf(0.8) www traffic Zipf(0.5) 0.2 uniform, s< γ=c/m (normalized cache size)

10 File popularity is time- varying TV series Trending tweets Movie popularity

11 Main Challenge Small user population slow estimation of popularities small hit probability 50req/day/item Content provider origin server CDN cache 10req/day/item BS/small cell cache 0.1req/day/item

12 Shot Noise Model Shot Noise Model Inhomogeneous Poisson The shot height determines the intensity of the item requests at any given time Results in accurate hit probability computation IRM SNM Traverso- Ahmed- Garetto- Giaccone- Leonardi- Niccolini, Temporal locality in today s content caching: Why it matters and how to model it, ACM SIGCOMM, Olmos- Kauffmann- Simonian- Carlinet, Catalog dynamics: Impact of content publishing and perishing on the performance of a LRU cache, ITC 2014 Youtube Data

13 Optimal policy: Age- Based Thresholding (ABT) Define a threshold θ(γ c ) on estimated popularities Cache all contents with > θ(γ c ) Equivalently, cache all contents with N i > In some regimes, very close to using frequentist estimator

14 Impact of catalog dynamics Hit prob optimization γ=10% s=0.8 =20 L=1000 caching decisions cache size constraint Optimal policy storing the contents with largest a posteriori estimate Many contents regime => Threshold policy 1 20 req/day at global cache 0.02 req/day at local cache Leconte- P- Gkatzikis- Draief- Vassilaras- Chouvardas, Placing Dynamic Content in Caches with Small Population, IEEE INFOCOM LRU caches don t work at base station level!

15 Caching architecture that exploits aggregation Estimate here Gated- LRU

16 Evidence of spatial locality on traffic volumes Traffic signature S4 Paris locations with signature S4 Fourno- Naboulsi- Stanica- Fiore, A Tale of 10 cities: Mobile Traffic Signatures of Urban Fabrics Can we expect the same in request sequences?

17 Learn in clusters: accuracy vs aggregation Cluster 2 Cluster 1

18 Model of spatial correlations What is the price to pay when aggregating data from multiple BS? Content popularity is locality dependent Ø Aggregation washes out locality (generalized) Stochastic Block Model for spatial correlations Typical model used in recommender systems Global content popularity Content profiles X i Location profiles Y l Correlation kernel K(X i,y l ) Ø Local popularity Ø Capturing locality of popularity yields better hit ratio:

19 Performance of perfect clustering Standard clustering techniques So far evaluation by a stochastic block model Massive aggregation quick but inaccurate Little aggregation slow but accurate

20 Time- varying popularity: summary Aggregate requests to learn faster Cluster to exploit spatial locality

21 THE END See: Wireless Caching: Technical Misconceptions and Business Barriers, upcoming IEEE Communication Magazine P- Bastug- Land- Caire- Debbah

22 Other References [Che02] Hierarchical web caching systems: modeling, design and experimental results [Fricker12] A Versatile and Accurate Approximation for LRU Cache Performance [Traverso13] Temporal Locality in Today s Content Caching: Why It Matters and How to Model It [Olmos14] Catalog Dynamics: Impact of Content Publishing and Perishing on the Performance of a LRU Cache [Gkitzenis13] Asymptotic Laws for Joint Content Replication and Delivery in Wireless Networks [Golrezaei12] FemtoCaching: Wireless Video Content Delivery through Distributed Caching Helpers [Nemhauser78] An analysis of approximations for maximizing submodular set functions [Arya01] Local Search Heuristics for k- median and Facility Location Problems [Qiu01] On the placement of web server replicas [Karger97] Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web

23 Backup material