1. Scalable Mobile Backhauling with Information-Centric Networking Luca Muscariello Orange Labs Networks Network Modeling and Planning and IRT SystemX. Joint work with G. Carofiglio, M. Gallo, D. Perino, Bell Labs, Alcatel-Lucent

2. motivation  trends  Content-centric nature of Internet usage highlights inefficiencies of the host-centric transport model  Higher costs in mobile infrastructure to sustain traffic growth with no innovation at network layer  Reduced margins for MNOs (…ok in Europe!)  ISP countermeasures  Quest for novel business opportunities in service delivery value chain  Increased network control to lower costs: network cost optimization is constrained to the ‘Traffic Engineering Triangle’

3. outline  mobile backhaul opportunities  evaluation scenario and results  introducing ICN in today’s mobile backhaul

4. outline  mobile backhaul opportunities  evaluation scenario and results  introducing ICN in today’s mobile backhaul

5. 5  objective : need for innovative network solutions to cope with huge mobile traffic growth with no significant capacity upgrades  tool : real traffic observations from our network and joint BL/OL experimental campaign over ~100 nodes with real workload/topology  achievements : our ICN design provides a content-aware network substrate in the mobile backhaul, compatible with 3GPP standard WHERE scalable mobile backhaul with ICN

6. 6 WHERE We focus on HTTP transactions of the following predominant applications In one peak hour for a set of macro cells covering a metro area.  web browsing  audio/video  You Tube ‒ cacheability: % of requests of objects requested at least twice in a given time period. ‒ In average 52% of total requests are cacheable ‒ Audio/video applications and You Tube in particular can attain values up to 86% traffic observations in the backhaul

7. 7 WHERE  a fraction of web transactions download the whole content (max). We evaluate the required cache budget (left) and the related percentage of saved bandwidth during the peak hour (right).  If we consider a whole day peak period and the entire national BH we attain few TB of useful storage.  By using three times larger but still limited memories, traffic can be reduced up to 95% during the peak hour. potential bandwidth savings

8. outline  mobile backhaul opportunities  evaluation scenario and results  introducing ICN in today’s mobile backhaul

9. outline  mobile backhaul opportunities  evaluation scenario and results  introducing ICN in today’s mobile backhaul

10. Methodology  We need to experiment with the full stack of protocols –CS/PIT/FIB –caching, queuing –flow-control, congestion control,  Realistic experiments –realistic workload  Repeatable experiments –control your 100% of your experiment –run and monitor it continuously

11. Lurch  A newly designed protocol need to be tested  Event driven simulation:  limited in the number of events (hence topology size)  computation is hard to parallelize  Large scale experiments:  Complex to manage  We needed a test orchestrator From protocol design to large scale experimentation

12. Lurch  Lurch is a test orchestrator for CCNx 1 (soon CCN-lite and NFD)  Simplify and automate ICN’s protocol testing over a list of interconnected servers (i.e. G5K).  Lurch run on a separate machine and control the test Controller

13. Lurch Application Control Plane Virtualized Data Plane Virtualized Data Plane Management CCNx TCP/UDP Virtualized IP IP layer PHY layer Data Plane Protocol stack Architecture  Lurch controller:  Virtualized Data plane  Control Plane  Application layer

14. Lurch  Create virtual interfaces between nodes (i.e. G5K)  Bash configuration file computed remotely by the orchestrator and transfered to experiment nodes  Network iptunnels to build virtualized interfaces  One physical interface (eth0), multiple virtual interfaces (tap0,..,) Topology management #!/bin/bash sysctl -w net.ipv4.ip_forward=1 modprobe ipip iptunnel add tap0 mode ipip local 172.16.49.50 remote 172.16.49.5 ifconfig tap0 10.0.0.2 netmask 255.255.255.255 up route add 10.0.0.1 tap0 iptunnel add tap1 mode ipip local 172.16.49.50 remote 172.16.49.51 ifconfig tap1 10.0.0.3 netmask 255.255.255.255 up route add 10.0.0.4 tap1 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. tap0 tap1 eth0 172.16.9.50 172.16.49.5 172.16.49.51 10.0.0.2 10.0.0.3 tap0 10.0.0.1 tap0 10.0.0.4 Controller Virtual Physical

15. Lurch  Remotely assign network resources to nodes preserving physical bandwidth constraints  Bash configuration file computed remotely by the orchestrator and transferred to experiment nodes  Traffic Control Linux tool to limit bandwidth, add delay, packet loss, etc.. Resource management #!/bin/bash tc qdisc del dev eth0 | cut -d " " -f 1) root tc qdisc add dev eth0 | cut -d " " -f 1) root handle 1: htb default 1 tc class add dev eth0 | cut -d " " -f 1) parent 1: classid 1:1 htb rate 100.0mbit ceil 10.0mbit tc filter add dev eth0 | cut -d " " -f 1) parent 1: prio 1 protocol ip u32 match ip dst 172.16.49.5 flowid 1:1 tc class add dev eth0 | cut -d " " -f 1) parent 1: classid 1:2 htb rate 100.0mbit ceil 50.0mbit tc filter add dev eth0 | cut -d " " -f 1) parent 1: prio 1 protocol ip u32 match ip dst 172.16.49.51 flowid 1:2 1. 2. 3. 4. 5. 6. 7. 8. 9. 10Mbps Controller Virtual Physical 50Mbps 1Gbps

16. Lurch  Remotely control name-based forwarding tables  Bash configuration file computed remotely by the orchestrator and transferred to experiment nodes  CCNx’s FIB control command ccndc Name-based control plane #!/bin/bash ccndc add ccnx:/music UDP 10.0.0.1 ccndc add ccnx:/video UDP 10.0.0.4 1. 2. 3. 4. 5. Name prefix face ccnx:/music0 ccnx:/video1 FIB ccnx:/music Controller Virtual Physical ccnx:/video

17. Lurch  Remotely control experiment workload  File download application started according experiment’s needs  Arrival process: Poisson,CBR…  File popularity: Zipf, Weibull, et..  Trace driven Application Workload  Two ways:  Centralize workload generation at the controller  Delegated workload generation to clients for performance improvement tap0 tap1 eth0 172.16.9.50 172.16.49.5 172.16.49.51 10.0.0.2 10.0.0.3 tap0 10.0.0.1 tap0 10.0.0.4 Controller Virtual Physical

18. Lurch  Remotely control experiment statistic’s  Bash start/stop commands sent remotely  CCNx’s statistics (e.g. caching, forwarding) through logs  top / vmstat monitoring active processes CPU usage (e.g. ccnd)  Ifstat monitoring link rate Measurements  At the end of the experiment statistics are collected and transferred to the user tap0 tap1 eth0 172.16.9.50 172.16.49.5 172.16.49.51 10.0.0.2 10.0.0.3 tap0 10.0.0.1 tap0 10.0.0.4 Virtual Physical Controller

19. EXPERIMENTS Running large scale experimentation on Content-Centric Networking via the Grid’5000 platform

20. Experiments  Large topologies  Up to 100 physical nodes  More than 200 links  Realistic scenarios  Mobile Backhaul

21. 21 WHERE A down-scaled model of a backhaul network.  4 “regional” PDN GWs connected by a full mesh  SGWs are assumed to be co-located with the PDN-GW  2 CDN servers external to the backhaul, reached via two PDN-GWs  each PDN-GW is the root of a fat tree topology composed of 20 nodes  eNodeBs aggregate traffic generated by three adjacent cells  every eNodeB serves the same average traffic demand network topology

22. 22 WHERE  Software: -We used an ICN prototype (http://www.ccnx.org)http://www.ccnx.org -with optimized distributed congestion control and multipath forwarding mechanisms (Carofiglio et al. IEEE ICNP 2013), based on decomposition Lagrangian multipliers with physical meaning: - network latency (measured in CCN/NDN by request/reply) - network node flow rate unbalance (registered in the pending request table) -LRU data replacement, cache along the path (dumb caching).  Experimental Testbed: -On the Grid 5000 -Bootable customized kernels with our network prototype -Lurch: our network experiment orchestrator (i.e. statistics collection, etc. ).  Workload: -Down-scaling of the traffic characterization obtained from Orange traces -Requests are aggregated at macro cell level methodology

23. 23 the platform

24. 24 WHERE  we compare at equal cache budget –Baseline –Traffic is routed through a single shortest path. –ICN –ICN transport, multi-path forwarding and LRU caching –PDNCache –Caches are deployed at PDN GWs only. –Traffic is routed through a single shortest path. –eNodeBCache –Caches are deployed at eNodeBs only. –Traffic is routed through a single shortest path. –ICN + PDNCache evaluated solutions

25. 25 results – latency reduction WHERE  ICN shows the better QoE in terms of delivery time  Improved user QoE due to:  in-network caching.  dynamic multipath transfer. ― a factor 3 reduction in average delivery time

26. 26  ICN sensibly decreases bandwidth utilisation inside the mobile backhaul w.r.t. alternative solutions, allowing potential cost reduction in the backhaulfrom outside the backhaul –up to 40% bandwidth savings in backhaul. WHERE results – bandwidth savings

27. 27 results - enhancing network flexibility WHERE  We emulate a flash crowd phenomenon on a link and compare the link load over time for ICN and for the baseline scenario without caching: ICN link load and average delivery time are almost not impacted by the flash crowd (in virtue of transport/caching interplay and multipath).

28. outline  mobile backhaul opportunities  evaluation scenario and results  introducing ICN in today’s mobile backhaul

29. 29 integrating ICN in today’s backhaul WHERE ICN HEADER INTRODUCTION Two alternatives: 1.in GTP-U encapsulation 2.After IP (IPsec) header with a specific protocol value ICN DATA DELIVERY PROCESS Two alternatives: a)ICN proxy co-located with eNodeB (with DPI) b)HTTP plugin at end-user POLICY-CHARGING Every node sends periodical reports to control plane elements via ad-hoc GTP-C functions about traffic statistics

30. conclusion and current work  ICN allows to remove anchoring to manage mobility  Mobility is not a technical problem  Communication is connection-less  Multi-path, multi-homing, multi-cast are native  In-network caching is native and outperforms PoP caching  Currently high-speed prototype at Alcatel-Lucent (40Gbps)  Ongoing discussion on ALU 7750 edge router…  Demonstrations: –Common demonstration at Bell Labs Future X Days in September 2014 –Demonstration at ACM SIGCOMM ICN 2014 to be held in Paris, September 24-26

31. Questions

32. 1. G. Carofiglio, M. Gallo, L. Muscariello, Bandwidth and storage sharing performance in information- centric networking, in ACM SIGCOMM ICN 2011 workshop, Toronto, Canada. 2. G. Carofiglio, M. Gallo, L. Muscariello, D.Perino, Modeling data transfer in content-centric networking, in Proc. of 23rd International Teletraffic Congress, ITC23 San Francisco, CA, USA, 2011 3. G. Carofiglio, M. Gallo, L. Muscariello, ICP: design and evaluation of an Interest control Protocol for Content-Centric networks, IEEE INFOCOM NOMEN WORKSHOP, Orlando, USA, March 2012 4. G. Carofiglio, M. Gallo, L. Muscariello, Joint Hop-by-Hop and Receiver Driven Interest control Protocol for content-Centric Networks, in ACM SIGCOMM workshop on information-centric networking, Helsinki, Finland, 2012, awarded as best paper. 5. G. Carofiglio, M. Gallo, L. Muscariello, On the Performance of Bandwidth and Storage Sharing in Information-Centric Networks, Elsevier Computer Networks, 2013. 6. G. Carofiglio, M. Gallo, L. Muscariello, D. Perino,Evaluating per-application storage management in content-centric networks, Elsevier Computer Communications: Special Issue on Information-Centric Networking, 2013. 7. M. Gallo, B. Kaumann, L. Muscariello, A. Simonian, C. Tanguy, Performance Evaluation of the Random Replacement Policy for Networks of Caches, Elsevier Performance Evaluation, 2013. 8. G. Carofiglio, M. Gallo, L. Muscariello, M. Papalini, Multipath Congestion Control in Content-Centric Networks In proc. of IEEE INFOCOM, NOMEN Workshop, Turin, Italy, April 2013. 9. G. Carofiglio, M. Gallo, L. Muscariello, M.Papalini, S. Wang Optimal Multipath Congestion Control and Request,Forwarding in Information-Centric Networks To appear in proc. of IEEE ICNP, Goettingen, Germany, October 2013. 10. White Paper in collaboration with Bell Labs, SCALABLE MOBILE BACKHAULING VIA INFORMATION CENTRIC NETWORKING. A glimpse into the benefits of an Information Centric Networking approach to data delivery., 2013 publications