Download ppt - Green Networking Jennifer Rexford Computer Science Department Princeton University

Green Networking

Jennifer RexfordComputer Science Department

Princeton University

Router Energy Consumption

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Internet Infrastructure

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router

link

Router Energy Consumption

• Millions of routers in the U.S.– Several Tera-Watt hours per year– $2B/year electric bill

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Line cards draw ~ 100W

(Source: National Technical Information Service, Department of Commerce, 2000. Figures for 2005 & 2010 are projections.)

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2.4

3.9

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2000 2005 2010

TwH/year200-400 W

Opportunities to Save Energy

• Networks over-provisioned with extra capacity

• Diurnal shifts in traffic due to user behavior

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Powering Down the Network

• Equipment is not energy proportional– Energy is nearly independent of load

• Turning off parts of the network– Entire router– Individual interface card

• While avoiding transient disruptions– Data traffic relies on the underlying network– Failures lead to transient packet loss and delay

6Shut down routers and interfaces without disruptions

Brief Background on Routers

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Router Architecture

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Switching Fabric

Processor

Line card

Line card

Line card

Line card

Line card

Line card

data plane

control plane

Data, Control, and Management

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Data Control Management

Time-scale

Packet (nsec)

Event (10 msec to sec)

Human (min to hours)

Tasks Forwarding, buffering, filtering, scheduling

Routing, signaling

Analysis, configuration

Location

Line-card hardware

Router software

Humans or scripts

Data Plane: Router Line Cards

• Interfacing – Physical link– Switching fabric

• Packet handling– Packet forwarding– Decrement time-to-live– Buffer management– Link scheduling– Packet filtering– Rate limiting 10

to/from link

to/from switch

lookup

Rec

eive

Transm

it

Control Plane: Routing Protocols

• Routing protocol– Routers talk amongst themselves– To compute paths through the network

• Routing convergence– After a topology change– Transient period of

disagreement– Packets lost, delayed,

or delivered out-of-order– Major disruptions to application performance 1111

The Rest of the Talk: Two Ideas

• Power down networking equipment– To reduce energy consumption– While minimizing disruption to applications

• Power down a router– Virtual router migration– Similar to virtual machine migration

• Power down an interface– Shutting down cables in a bundled link– Similar to dynamic frequency voltage scaling

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VROOM: Virtual ROuters On the Move

Joint work with Yi Wang, Eric Keller, Brian Biskeborn, and Kobus van der Merwe (AT&T)

http://www.cs.princeton.edu/~jrex/papers/vroom08.pdf

Virtual ROuters On the Move

• Key idea– Routers should be free to roam around

• Useful for many different applications– Reduce power consumption– Simplify network maintenance– Simplify service deployment and evolution

• Feasible in practice– No performance impact on data traffic– No visible impact on routing protocols

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The Two Notions of “Router”• IP-layer logical functionality, and physical equipment

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Logical(IP layer)

Physical

Tight Coupling of Physical & Logical• Root of many network-management challenges (and

“point solutions”)

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Logical(IP layer)

Physical

VROOM: Breaking the Coupling• Re-mapping logical node to another physical node

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Logical(IP layer)

Physical

VROOM enables this re-mapping of logical to physical through virtual router migration.

Case 1: Power Savings

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• Contract and expand the physical network according to the traffic volume


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Case 2: Planned Maintenance

• NO reconfiguration of VRs, NO reconvergence

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A

B

VR-1



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A

B

VR-1



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A

B

VR-1

Case 3: Service Deployment/Evolution

• Move (logical) router to more powerful hardware

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Case 3: Service Deployment/Evolution

• VROOM guarantees seamless service to existing customers during the migration

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Virtual Router Migration: Challenges

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1. Migrate an entire virtual router instance• All control plane & data plane processes / states


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1. Migrate an entire virtual router instance2. Minimize disruption

• Data plane: millions of packets/sec on a 10Gbps link• Control plane: less strict (with routing message retrans.)


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1. Migrating an entire virtual router instance2. Minimize disruption3. Link migration


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1. Migrating an entire virtual router instance2. Minimize disruption3. Link migration

VROOM Architecture

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Dynamic Interface Binding

Data-Plane Hypervisor

• Key idea: separate the migration of control and data planes

1.Migrate the control plane2.Clone the data plane3.Migrate the links

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VROOM’s Migration Process

• Leverage virtual server migration techniques• Router image

– Binaries, configuration files, etc.

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Control-Plane Migration

• Leverage virtual server migration techniques• Router image• Memory

– 1st stage: iterative pre-copy– 2nd stage: stall-and-copy (when the control plane

is “frozen”)

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• Leverage virtual server migration techniques• Router image• Memory

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Physical router A

Physical router B

DP

CP

• Clone the data plane by repopulation– Enable migration across different data planes– Avoid copying duplicate information

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Data-Plane Cloning

Physical router A

Physical router B

CP

DP-old

DP-newDP-new

• Data-plane cloning takes time– Installing 250k routes may take several seconds

• Control & old data planes need to be kept “online”• Solution: redirect routing messages through tunnels

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Remote Control Plane

Physical router A

Physical router B

CP

DP-old

DP-new

• Data-plane cloning takes time– Installing 250k routes takes over 20 seconds


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Physical router A

Physical router B

CP

DP-old

DP-new

• Data-plane cloning takes time– Installing 250k routes takes over 20 seconds


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Physical router A

Physical router B

CP

DP-old

DP-new

• At the end of data-plane cloning, both data planes are ready to forward traffic

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Double Data Planes

CP

DP-old

DP-new

• With the double data planes, links can be migrated independently

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Asynchronous Link Migration

A

CP

DP-old

DP-new

B

• Virtualized operating system– OpenVZ, supports VM migration

• Routing protocols– Quagga software suite

• Packet forwarding– Linux kernel (software), NetFPGA (hardware)

• Router hypervisor– Our extensions for repopulating data plane,

remote control plane, double data planes, …

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Prototype Implementation

• Experiments in Emulab– On realistic Abilene Internet2 topology

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Experimental Evaluation

• Data traffic– Linux: modest packet delay due to CPU load– NetFPGA: no packet loss or extra delay

• Routing-protocol messages– Core router migration (OSPF only)

• Inject an unplanned link failure at another router• At most one retransmission of an OSPF message

– Edge router migration (OSPF + BGP)• Control-plane downtime: 3.56 seconds• Within reasonable keep-alive timer intervals

– All routing-protocol adjacencies stay up43

Experimental Results

Where To Migrate

• Physical constraints– Latency

• E.g, NYC to Washington D.C.: 2 msec– Link capacity

• Enough remaining capacity for extra traffic– Platform compatibility

• Routers from different vendors– Router capability

• E.g., number of access control lists (ACLs) supported• Constraints simplify the placement problem

– By limiting the size of the search space

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Conclusions on VROOM

• VROOM: useful network-management primitive– Separate tight coupling between physical and logical– Simplify management, enable new applications

• Evaluation of prototype– No disruption in packet forwarding– No noticeable disruption in routing protocols

• Future work– Migration scheduling as an optimization problem– Extensions to hypervisor for other applications

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Greening Backbone Networks: Shutting Off Cables in Bundled Links

Joint work with Will Fisher and Martin Suchara

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http://www.cs.princeton.edu/~msuchara/publications/GreenNetsBundles.pdf

Power Down Links and Routers?• Larger round-trip time (RTT)• Slow convergence process

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Bundled Links in Backbone Networks

• Links come in bundles– Incremental upgrades, equipment costs, …– Around 2-20 cables per link

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Powering All Cables is Wasteful

• Only power the cables that are needed– Reduce energy consumption, without disruption

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30-40% utilization

Optimization Problem

• Management-plane optimization problem– Input: network configuration and load– Output: list of powered cables

• Integer linear program

• NP hard need heuristics50

min # powered cabless.t. link loads ≤ capacities

flow conservation carries all traffic demands

Related Tractable Problem

• If energy was proportional to link load?

• Minimize sum of link loads– Rather than the number of powered cables– Leads to a fractional linear program

• Benefits of this problem– Computationally tractable– Upper and lower bound on power saving– Starting point for heuristics

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First Attempt: Naïve Solution

• Always “round up”

• Up to n times worse where n = # of routers

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→

Fast Greedy Heuristic

• Solve fractional problem and “round up”

• Identify link with the most “rounding up”• Round down and remove an extra cable• Repeat if a feasible solution exists

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→

Other heuristics: Explore combinations of links

Experimental Set-Up

• Measure– Energy savings and computational time

• Solving linear program– AMPL/CPLEX

• Varying– Offered load and number of cables

• Topologies– Abilene with measured demands– Waxman graph with synthetic demands

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Energy Savings in Abilene

• Energy savings depends on the bundle size 55

ener

gy

savi

ng

s (%

)

bundle size

Turn entire link on or off

Similar performance of heuristics

Computation Time

• FGH suited to real-time computation– Reoptimize on/off cables during the day– Other heuristics are expensive for only small gain

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Conclusion on Bundled Links

• Power down some cables in a bundle– Minimize energy consumption– Without disrupting data traffic

• Design and evaluation of heuristics– Significant energy savings– Low computational complexity– Simple heuristics are quite effective

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Conclusion of the Talk• Network energy consumption

– Routers consume a lot of energy– Routers are not energy proportional– Selectively powering down is effective

• Two main ideas– New mechanism: virtual router migration– New optimization: identify cables to power down

• Future work– Toward energy-proportional routers– Network designs that minimize server energy 58