scalable mobile backhauling with information-centric networking luca muscariello orange labs...
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Scalable Mobile Backhaulingwith Information-Centric Networking
Luca Muscariello Orange Labs NetworksNetwork Modeling and Planningand IRT SystemX.
Joint work with G. Carofiglio, M. Gallo, D. Perino, Bell Labs, Alcatel-Lucent
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’
outline
mobile backhaul opportunitiesevaluation scenario and results introducing ICN in today’s mobile backhaul
outline
mobile backhaul opportunitiesevaluation scenario and results introducing ICN in today’s mobile backhaul
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
WHEREscalable mobile backhaul with ICN
6
WHERE
We focus on HTTP transactions of the following predominant applicationsIn 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
outline
mobile backhaul opportunitiesevaluation scenario and results introducing ICN in today’s mobile backhaul
outline
mobile backhaul opportunitiesevaluation scenario and results introducing ICN in today’s mobile backhaul
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
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
Lurch
Lurch is a test orchestrator for CCNx1 (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
Lurch
Application
Control Plane
Virtualized Data Plane
Managem
ent
CCNx
TCP/UDP
Virtualized IP
IP layer
PHY layer
Data PlaneProtocol stack
Architecture Lurch controller:
Virtualized Data plane Control Plane Application layer
Lurch
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=1modprobe ipip
iptunnel add tap0 mode ipip local 172.16.49.50 remote 172.16.49.5ifconfig tap0 10.0.0.2 netmask 255.255.255.255 uproute add 10.0.0.1 tap0
iptunnel add tap1 mode ipip local 172.16.49.50 remote 172.16.49.51ifconfig tap1 10.0.0.3 netmask 255.255.255.255 uproute add 10.0.0.4 tap1
1.2.3.4.5.
6.7.
8.
9.10.
tap0
tap1
eth0 eth0 eth0
172.16.9.50 172.16.49.5172.16.49.51
10.0.0.2
10.0.0.3
tap010.0.0.1
tap010.0.0.4
Controller
Virt
ual
Phy
sica
l
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) roottc 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 10.0mbittc 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
Virt
ual
Phy
sica
l
50Mbps
1Gbps
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:/music 0
ccnx:/video 1
FIB
ccnx:/music
Controller
Virt
ual
Phy
sica
l
ccnx:/video
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 eth0 eth0
172.16.9.50 172.16.49.5172.16.49.51
10.0.0.2
10.0.0.3
tap010.0.0.1
tap010.0.0.4
Controller
Virt
ual
Phy
sica
l
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 eth0 eth0
172.16.9.50 172.16.49.5172.16.49.51
10.0.0.2
10.0.0.3
tap010.0.0.1
tap010.0.0.4
Virt
ual
Phy
sica
l
Controller
EXPERIMENTS
Running large scale experimentation on Content-Centric Networking via the Grid’5000 platform
Experiments
Large topologies Up to 100 physical nodes More than 200 links
Realistic scenarios Mobile Backhaul
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
WHERE
Software:-We used an ICN prototype (http://www.ccnx.org)-with optimized distributed congestion control and multipath
forwarding mechanisms (Carofiglio et al. IEEE ICNP 2013), based on
decompositionLagrangian 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
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
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results – latency reduction WHERE
ICN shows the better QoE in terms of delivery timeImproved user QoE due to:
in-network caching.dynamic multipath transfer.
―a factor 3 reduction in average delivery time
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ICN sensibly decreases bandwidth utilisation inside the mobile backhaul w.r.t. alternative solutions, allowing potential cost reduction
in the backhaul from outside the backhaul
–up to 40% bandwidth savings in backhaul.
WHEREresults – bandwidth savings
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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).
outline
mobile backhaul opportunitiesevaluation scenario and results introducing ICN in today’s mobile backhaul
29
integrating ICN in today’s backhaul WHERE
ICN HEADER INTRODUCTIONTwo alternatives:1. in GTP-U encapsulation 2. After IP (IPsec) header with a specific protocol value
ICN DATA DELIVERY PROCESSTwo alternatives:a) ICN proxy co-located with eNodeB (with DPI)b) HTTP plugin at end-user
POLICY-CHARGINGEvery node sends periodical reports to control plane elements via ad-hoc GTP-C functions about traffic statistics
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
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
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