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IS THERE A CASE FOR MOBILE PHONE CONTENT PRE-STAGING? Santa Barbara, December 9-12, 2013 Alessandro Finamore Marco Mellia Zafar Gilani Konstantina Papagiannaki.

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Presentation on theme: "IS THERE A CASE FOR MOBILE PHONE CONTENT PRE-STAGING? Santa Barbara, December 9-12, 2013 Alessandro Finamore Marco Mellia Zafar Gilani Konstantina Papagiannaki."— Presentation transcript:

1 IS THERE A CASE FOR MOBILE PHONE CONTENT PRE-STAGING? Santa Barbara, December 9-12, 2013 Alessandro Finamore Marco Mellia Zafar Gilani Konstantina Papagiannaki Yan Grunenberger Vijay Erramilli Politecnico di Torino Universitat Polite ̀ cnica de Catalunya

2 Caching in mobile networks Gn Proxy-cache Two solutions for saving volumes Forward caching: add proxy-cache in the network core Reduce traffic volume only towards the Internet Savings are driven by the cache hit-ratio which is typically ~30% (1) Cache on device: reserve specific storage for caching on users device Reduces wireless link traffic on a per-user base The same object requested by different devices traverses the wireless link at least once for each device (1) [AT&T – IEEE Int. Comp journal 11] To cache or not to cache: The 3g case local cache 2

3 Caching in mobile networks Gn Proxy-cache Two solutions available for caching in mobile networks Forward caching: add proxy-cache in the network core Reduce traffic volume only towards the Internet Savings are driven by the cache hit-ratio which is typically ~30% (1) Cache on device: reserve specific storage for caching on users device Reduces wireless link traffic on a per-user base The same object requested by different devices traverses the wireless link at least once for each device (1) [AT&T – IEEE Int. Comp journal 11] To cache or not to cache: The 3g case local cache 3 Both are important but none of them really address the most expensive part of the network: wireless channel Both are important but none of them really address the most expensive part of the network: wireless channel

4 Key idea: content pre-staging 4 Gn Proxy-cache local cache Bundle creator The Bundle creator periodically creates a bundle of popular objects The bundle is pre-staged to users device using a broadcast channel Users device store and consume the bundle locally Push-based system: transmit 1copy to serve requests of multiple users We would like to mute as many requests as possible

5 …but is it worth? 5 Rather than designing the system as a whole, we want to quantify the potential gains the system would offer Research questions addressed in this work: Is the content popularity skewed enough to allow gain? How large should the bundle be to lead to savings? What are the achievable saving? Any benefits for the users?

6 Real data: geo-tagged HTTP logs 6 Real trace collected from a major European mobile carrier 1 day from a large metropolis HTTP logs created by forward caches Anonymised terminal ID, URL, downloaded volume Cached-flag, indicating hit/miss by forward caches Cell-ID, indicating from which cell requests are issued

7 Good news: content is pre-stageable! 7 Popularity: the top 1,000 requested objects account for 15% of byte-wise volume and for 48% of req. are accessed at least once by 80% of customers Cacheability: ~40% of objects are cached by proxies for the evaluation we consider a conservative and a optimistic scenarios Long lifetime: 95% of cacheable objects have a lifetime >1hour

8 Which bundle creator strategies? 8 What to bundle? Max-Req: bundle top-N most popular objects Max-Vol: bundle top-N objects generating the largest volume Weighted-Vol: bundle big and popular objects How often to broadcast? Engineering choice: every hour is a reasonable value 1. Create the bundle based on the traffic between 5pm-6pm 2. Broadcast the bundle at 6pm 3. Users terminals enjoy the content between 6pm-7pm Same bundle broadcasted to all cells

9 3,000 objects are enough to get saving 9 With simply 3,000 objects we can achieve savings Max-Vol has the highest volume savings (>13%, conservative)... but how big is the bundle? 0.13 For Max-Vol 3,000 objects = >500MB!!! For Max-Vol 3,000 objects = >500MB!!!

10 10 With simply 3,000 objects we can achieve savings Max-Vol has the highest volume savings (>13%, conservative)... but how big is the bundle? 3,000 objects = more than 500MB It is not a practical solution Max-Req performs as Weighted-Vol but has a simpler logic 0.13 For Max-Vol 3,000 objects = >500MB!!! For Max-Vol 3,000 objects = >500MB!!! ,000 objects are enough to get saving

11 11 Overall, Max-Req is the best strategy: With 3,000 objects = only 34MB 7% volume saved (conservative) 11% requests saved (conservative) MB = at least 7% of savings

12 Saving are stable over time 12 Results stable across the day!!!

13 15% of users become totally silent 13 20% of users does not benefit Heavy hitters downloading only few big (and unpopular) objects 15% of users enjoys 100% of savings Users accessing GPS and navigation services Similar results for num. of requests (details in the paper)

14 Spatial correlation 14 What if we consider a per-tower bundle? Consider 2 towers (periphery and downtown) of the top10 towers generating the largest number of requests and volume Focus on peak hour per-tower bundle is sub-optimal

15 Conclusions & Future work 15 Results show that pre-staging could be an opportunity to optimize wireless capacity BUT Reception costs and other aspects need further investigation Device energy consumption System engineering Spatial and temporal correlations Both content providers and users can collaborate with the system to estimate content popularity Returns depend also on costs and the wiliness of operators to invest in such technology

16 16 ## || ?? Alessandro Finamore

17 17 With simply 3,000 objects we can achieve savings Max-Vol has the highest volume savings (>13%, conservative)... but how big is the bundle? 3,000 objects = more than 500MB It is not a practical solution Weighted-Vol has similar savings than Max-Req but more complex logic ,000 objects = ~7% of volume saved Overall, Max-Req is the best strategy: With 3,000 objects = only 34MB 7% volume saved (conservative) 11% requests saved (conservative) (more details in the paper) Overall, Max-Req is the best strategy: With 3,000 objects = only 34MB 7% volume saved (conservative) 11% requests saved (conservative) (more details in the paper) Results stable across the day!!!

18 Data set & content properties 18 Traffic collected from a major European mobile carrier HTTP logs created by proxies Anonymized terminal ID, Requested URL, Downloaded volume, etc. 1 day (November 26 th, 2012) from a large metropolis Bundled content requires three properties: Popularity: the top 1,000 requested objects account for 15% of byte-wise volume and for 48% of requests are accessed at least once by 80% of customers Cacheability: 40% of objects found to be cached by proxies (conservative estimation) Long lifetime: Only 5% of cacheabile objects have a lifetime <1hour

19 Return of Investment (ROI) 19 We can define the Return Of Investment (ROI) ROI = = Benefit Volume saved CostBundle Size By devoting a (little) fraction of the capacity, it is possible to obtain very high return Notice the log-scale

20 Objects popularity Objects requests follow a Zipf distribution. Top requests objects are very popular among all users Top 1,000 objects account for ~15% of volume ~48% of requests Top 1,000 objects are requested at least once by >80% of users 20

21 Users savings Top100 21

22 Content properties 22 The system is based on the concept of the bundle To achieve saving, objects in bundle need to be Popular, so that they account for a large volume be Cacheable, so that can be stored on users devices present a long Lifetime, so reduce the broadcast frequency We need to very if such characteristics are true

23 Content popularity Number of requests per object follows a classic Zipf distribution The top 1,000 requested objects account for 15% of byte-wise volume and for 48% of requests Recurrence over time About 50% of the top 100 objects are still among the top 100 in the following hour The most requested objects are also popular among users? Top 1,000 objects are requested at least once by >80% of users 23 Top requested objects

24 Content cacheability Rely on the information obtained from the logs: Objects FLAGGED as cached are indeed cacheable Object NOT FLAGGED might still be cacheable but are not cached because of Constraints imposed by the proxy (e.g., cache size) Constraints imposed by content owners (e.g., YouTube videos) Overall, considering the top 10,000 requested objects, only 40% are cacheable Based on this information we consider two scenarios: Conservative: bundle contains only objects that are cacheble Optimistic: bundle contains any objects even if declared not cacheable 24

25 Content lifetime 25 HTTP logs do not report objects lifetime Solved by active experiments Consider only objects found to be cacheable Download objects and inspect HTTP caching directives in HTTP headers Only 5% of objects present a lifetime smaller than 1 hour

26 Savings (peak hour) 26 Few thousands objects allow to achieve some savings Max-Vol has the highest volume savings Max-Vol is not practical since it requires a very large bundle (3000 objects >= 500MB) …but how big is the bundle?

27 Savings (peak hour, 6pm-7pm) 27 Max-Req bundle size < 34MB for top 3k objects 7% vol. saved (conservative) 11% req. saved (conservative) Max-Req has the same performance of Weighted-Vol but has a simpler logic Few thousands objects allow to achieve some savings Max-Vol has the highest volume savings Max-Vol is not practical since it requires a very large bundle (3000 objects >= 500MB)


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