Distribute what you can, centralize what you must!

1. Distribute what you can, centralize what you must! Narseo Vallina-Rodriguez Supervisor: Jon Crowcroft Qualcomm – Cambridge22nd May 2013

2. Motivation • The web is becoming mobile • Apps rely on multiple online/cloud services (mobile mashup): • CDNs (Akamai) • Cloud services (Amazon WS) • Authentication APIs (Oauth) • Assisting sensors (A-GPS) • Advertisement (AdMob, Burstly, Millennial Media, …) • Push notifications (Google’s GCM) • NAT punching for P2P (Skype)

3. Research question How do mobile apps’ cloud dependency impact on cellular network and battery life of mobile handsets?

4. 2012-2013 outcome • When Assistance becomes Dependence: characterizing the costs and inefficiencies of A-GPS. Vallina-Rodriguez, Finamore, Grunenberger, Papagiannaki and Crowcroft. ACM SIGMOBILE MC2R (under review) • Breaking for Commercials: Characterizing Mobile Advertising. Vallina-Rodriguez, Finamore, Shah, Grunenberger, Haddadi, Papagiannaki and Crowcroft. In ACM Internet Measurement Conference 2012(IMC'12) • Energy Management Techniques in Modern Mobile Devices. Vallina-Rodriguez and Crowcroft. In IEEE Communications Tutorials and Surveys, 2012. • When David can help Goliath: the case for cellular augmentation of wired networks. Vallina-Rodriguez, Erramilli, Grunenberger, Gyarmati, Laoutaris, Stanojevic, Papagiannaki, In ACM HotNets'12 • Signposts: End-to-End Networking in a World of Middleboxes. Aucinas, Chaudhry, Crowcroft, ProbstEide, Hand, Madhavapeddy, Moore, Mortier, Rotsosand Vallina-Rodriguez. In ACM SIGCOMM 2012. DEMO

5. Take away: moving to the edge! • Mobile applications may abuse cellular networks: they cause network(signaling/channels/operational) and energy costs! • Fetching content in a centralized fashion is not the onlyway

6. Distribute as much as you can!

7. Flashlinq/LTE-direct • P2P wireless technology • Perfect candidate for transparent communication in the edge! • Peer discovery (energy efficient) • Expression-based discovery (service) • Always-on background service with low duty-cycle • Similar to powering up a paging channel every X seconds • Current prototype performance: • Low-latency (<10 ms) • Good throughput (~ 20 Mbps) • Discovery (1~2 seconds)

8. … but what can be distributed?

9. 1. Localized data

10. Use case 1: Localized data • A large fraction of mobile data is local • Weather • Notifications • Ads • Apps use cellular networks and push notifications to fetch this content • High latency • No delivery guarantees [Cellular data network infrastructure characterization and implication on mobile content placement, Xu et al. SIGMETRICS’2011]

11. Use case 1: Airport Use case 1: Airport notifications SERVER (UK) NODE B Google GCM (Ir) INTERNET RNC SGSN GGSN

12. Use case 1: Airport notifications Traffic Pattern Heathrow App For Android (Flight Update) • TCP/IP Push notification model is broken for local data: • Frequent RNC promotions (some caused by TCP Heartbeats) • Waste of energy, middleboxes/proxies memory and radio channels (+200K users/day, a lot of signaling traffic!) EnergySignalingSpectrum (HSPA)

13. Use case 1: Airport Use case 1: Airport notifications PubSub modelLow latency No net overhead Energy efficient No Middleboxes! SERVER (UK) Flashlinq NODE B Google GCM (Ir) INTERNET RNC SGSN GGSN

14. 2. Collaborative sensing

15. Use case 2: Collaborative A-GPS • Assisting data (time, ephemeris, almanac, coarse location) downloaded from network: • Reduces TTFF(usability) • Temporal validity up to 2 weeks for ephemeris • Problem: use of cellular network may impair performance and increase energy costs!

16. Use case 2: Collaborative A-GPS 2x current! Control-planelatency

17. Use case 2: Collaborative A-GPS • Collaboration between devices in a P2P fashion: • Context-awareness (sense environment so do not turn on AGPS indoors!) • Share/pre-fetch assisting data (reduces latency to fetch data) • Prototype for Nexus One: • Pre-fetch and cache of assisting data • Devices can detect if they’re indoorsin less than 10 seconds • Blackbox. Hard to inject assisting data on chipsets (A-GPS is controlled by binary/proprietary files/drivers )

18. 3. Wired-wireless integration

19. Use case 3: Wired-wireless integration • 3G offloading to WiFi and femtocells: • Reduce network traffic • No real benefit for users (unless volume cap in data-plan) • Wired network can be constrained! • Can cellular networks augment wired networks? • Wired nets deployment is $$$ • Cellular nets have good coverage

20. Use case 3: Wired-wireless integration • Cellular network can provide more capacity than wired ones (DSL) • Spare capacity on cellular network • Powerboostfor video-streaming apps • Use-and-release • Does NOTwork everywhereanytime! 4.7 Mbps DSLAM A 2 Km 2.8 Mbps Google Maps

21. Use case 3: Wired-wireless integration • 2x downlink/5x uplink for most locations with 1 mobile device • Simulation: 50% of the videos have a speed up factor of 10x

22. Conclusions • Current cloud-mobile model is not efficient • Hyper-centralized: push notifications • Lack of connectivity between handsets: missing opportunities • Cellular and wired networks are fully decoupled • Flashlinq/LTE-direct can bring a new mobile paradigm! • Energy and network efficient • Distributed • Flexible

23. Flashlinq limitations and extensions • Transparent security/authentication mechanisms • Lessons to be learnt from the past: Bluetooth and WiFi-direct failed! • Source of DoS/Privacy/Energy attacks • Global Signpost-ish naming (OpenSource, DNSSEC based) • Low-level radio details must be exposed to OS! • Too much layering hides inefficiencies: e.g. A-GPS and 3G • Cross-layer optimizations are key (e.g. iPhone vs. Android) • Incentives for operators? • Reduce operational costs: better use of limited capacity • Licensed frequency

24. Thank you for your attention! http://www.cl.cam.ac.uk/~nv240 nv240@cam.ac.uk