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Can coarse circuit switching work & What to do when it doesn't?. Jerry Chou Advisor: Bill Lin University of California, San Diego CNS Review, Jan. 14, 2009. Outline. Motivation Overview of new optical networking paradigm How to provision optical circuits? - PowerPoint PPT Presentation
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Can coarse circuit switching work & What to do when it doesn't?
Jerry ChouAdvisor: Bill Lin
University of California, San Diego
CNS Review, Jan. 14, 2009
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Outline
• Motivation
• Overview of new optical networking paradigm
• How to provision optical circuits?
• What to do when provision circuits not enough?
• Conclusions
3
Internet Traffic Ever Increasing
4
Current Packet Routing Scenario
• Packets electronically routed hop-by-hop– IP routers interconnected over switched optical backbone– OEO conversion and queuing delays at each hop
OXC
OXC
OXC
OXC
OXC
5
Optical Circuit Switching
• If optical circuit switching would work, then no intermediate per-hop queuing delays and OEO conversions = much faster
OXC
OXC
OXC
OXC
OXC
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Optical Switching Options
• Extremely difficult to implement packet buffers and logic in optics
• No viable dynamically reconfigurable active optical switches at this time scale
PacketSwitching
10 ns
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Optical Switching Options
• New signaling protocol and electronic control plane required to implement dynamic reservations
• Although active optical switches available at this time scale, coordination of such frequent network-wide reconfigurations not easy
PacketSwitching
10 ns
OpticalBurst
Switching
1 ms
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Optical Switching Options
• Can we reasonably predict the traffic so that we can provision optical circuits to carry them?
• Can we provide a “fall-back” mechanism when circuit capacity is enough?
PacketSwitching
10 ns
OpticalBurst
Switching
1 ms
Quasi-StaticOpticalCircuits
1 hr
Over 3 Million X
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Outline
• Motivation
• Overview of new optical networking paradigm
• How to provision optical circuits?
• What to do when provision circuits not enough?
• Conclusions
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Observation
• Aggregate traffic at the core is relatively smooth and variations are predictable
Source: Roughan’03 on a Tier-1 US Backbone
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Case Study
• On high-performance public backbone networks– Abilene (US):11 nodes, 23 links– GEANT (Europe): 23 nodes, 74 links– Public traffic matrices are available
• Optical circuits only change on hourly basis
• Use historical traffic to “predict” how much traffic will occur in the future– Abilene: 03/01/04-04/21/04, GEANT: 01/01/05–04/10/05
• Provision circuits to maximize likelihood that circuits have enough capacity
• Simulated actual traffic (over a week)– Abilene: 04/22/04-04/26/04, GEANT: 04/11/05–04/15/05
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Circuits
• Setup circuits possibly across multiple paths in physical layer
Seattle
Sunnyvale
Indianapolis
Denver
Los Angeles Kansas City
ChicagoNew York
Washington
Atlanta
Houston
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Circuits
• Logically one (optical) circuit for each OD-pair (origin-destination pair)
Seattle
New York
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Abilene Network• Drop rates is the percentage of offering traffic exceeding its
circuit capacity• To consider a highly utilized network, traffic is scaled, such that at
least one link is saturated under OSPF• Worst-case 6.41%, 0.33% on average, mostly at or near 0%
Circuit switching works “most of the time” if carefully provisionedCircuit switching works “most of the time” if carefully provisioned
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New Paradigm• Provision optical circuits that maximize the probability
of sufficient capacity to carry traffic
• Use optical circuit switching by default
• When actual traffic exceeds circuit capacities, route (electronically) over other “pre-configured circuits” with spare capacity
OXCOptical transit traffic
Traffic arriving tointermediate node
Smaller (simpler) routers
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Analogy
• Direct “non-stop” flights (optical circuits) by default• If overbooked, re-route (electronically) excess demand
through alternative multi-hop flights
Seattle NY
Houston
To:NY
To:HS
To:NY
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Abilene Network
• No packet drops with re-routing (adaptive load-balancing method to be discussed)
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Advantages of New Paradigm
• Minimize queuing delay and latency for packets
• Reduce workload on electronic routers
• Optical circuits change infrequently, and mechanisms exist to provision circuits
• Key idea is to re-route electronically excess traffic rather than “on-the-fly” dynamic optical circuit reconfigurations
• Avoid new signaling protocol and frequent coordination of network-wide reconfigurations
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Outline
• Motivation
• Overview of new optical networking paradigm
• How to provision optical circuits?
• What to do when provision circuits not enough?
• Conclusions
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Basic Idea
• Use historical traffic data sets to decide on bandwidth allocation– Major ISPs have data collection infrastructure already
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Ideally, Traffic is Stable
• Abilene– 11 nodes connected by 10Gb/s links
Seattle
Sunnyvale
Indianapolis
Denver
Los Angeles Kansas City
Chicago New York
Washington
Atlanta
Houston
Seattle/NY:Always 5Gb/sAllocate: 5Gb/s
Sunnyvale/Houston:Always 5Gb/sAllocate: 5Gb/s
Both flows can be carried by provisioned circuits
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But, Flows Fluctuate Differently
• Abilene– 11 nodes connected by 10Gb/s links
Seattle
Sunnyvale
Indianapolis
Denver
Los Angeles Kansas City
Chicago New York
Washington
Atlanta
Houston
Seattle/NY:High traffic meanLow traffic variance
Sunnyvale/Houston:Low traffic meanHigh traffic variance
Give more bandwidth to flows with “high mean” or “high variance”?
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Circuit Provisioning Approach
• Use Cumulative Distribution Function (CDF) as “utility function” (predictor of “acceptance probability”)
• Acceptance probability– The probability of a provisioned circuit with enough capacity
to carry its offering traffic
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Example
• Abilene– 11 nodes connected by 10Gb/s links
Seattle
Sunnyvale
Indianapolis
Denver
Los Angeles Kansas City
Chicago New York
Washington
Atlanta
Houston
Seattle/NY:90% time ≤ 6Gb/s50% time ≤ 4Gb/sAllocate: 6Gb/s
Sunnyvale/Houston:90% time ≤ 6Gb/s80% time ≤ 4Gb/sAllocate: 4Gb/s
Seattle/NY has 90% acceptance probability
Sunnyvale/Houston has 80% acceptance probability
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Circuit Provisioning Approach
• Formulate bandwidth allocation (circuit provisioning) as multi-path utility max-min fair allocation problem
– Utility functions represent traffic statistics (generally utility functions can be non-linear)
– Max-min fairness reach balance between throughput and fairness
– Multi-path circuits provide more freedom and better performance
We provide the first solution to the multi-path utility max-min fair
allocation
We provide the first solution to the multi-path utility max-min fair
allocation
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Multi-path Utility Max-min Algorithm• Allocation based on “water-filling algorithm” and
maximum concurrent flow
• Steps:1. Identify maximum common utility increment 2. Solve maximum concurrent flow problem to find multi-
path routing3. Identify saturated flow
Max utility
Fill-up by with a routing
Saturated flow
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Multi-Path vs. Single-Path
• Significantly lower drop probability– Mean drop rate: 3.56% vs. 20.34%– Max drop rate: 18.25 vs. 34.72%
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Outline
• Motivation
• Overview of new optical networking paradigm
• How to provision optical circuits?
• What to do when provision circuits not enough?
• Conclusions
29
r(C) = 20
• Localized approach:– load-balance on outbound circuits, weighted by
spare capacity
r(B) = 30
r(D) = 25
B
C
DA
1. r(B) < B[A, B] ?YES
NO
2. k = random (wk)
Optical Circuit
3535
35
Problem1: greedy solution based only one-hop info.Problem2: oscillation of weight changes can happen
Problem1: greedy solution based only one-hop info.Problem2: oscillation of weight changes can happen
Adaptive Load-Balanced Routing
30
Adaptive Load-balance Re-routing
• Distributed approach: Step1: Compute path cost by Distance-Vector-like protocol Step2: Update weights to reach Wardrop Equilibrium state
– Every interval only shift weight by a small fraction δ– Achieve fast converge and prevent oscillation– Based on selfish routing no coordination among nodes
s t1
1
4
32
5
1
12 1
Current weights: w1, w2
δ = f (C1, C1, w1, w2)w1 = w1 + δ, w2 = w2
- δ
path1 cost(C1): (1+4)=5path2 cost(C2): (1+8)=9
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Abilene Network
• 90 percentile drop rate comparison– OSPF has 0% drop at scale factor of 1
32
Abilene Network
• 90 percentile drop rate comparison– Cisco’s “ecmp” load-balances across equal cost shortest
paths and achieve lower drop rate
33
Abilene Network
• 90 percentile drop rate comparison– Without rerouting, we suffer small drop rates even at the
scale factor of 1– But show lower drop rates at larger scale factors b.c of
greater path diversity and better load-balance
34
Abilene Network
• 90 percentile drop rate comparison– Achieve lowest drop rates among all– With rerouting, we don’t have drop until at a factor of
1.75.
35
Abilene Network
• Circuit provisioning achieve lower drop rates under high traffic load b.c of load-balanced routing path
• Rerouting effectively reduce drop rates under low traffic load by utilizing residual network capacity
36
Outline
• Motivation
• Overview of new optical networking paradigm
• How to provision optical circuits?
• What to do when provision circuits not enough?
• Conclusions
37
Conclusion• A new paradigm of optical circuit switching by default,
packet routing when necessary
• Formulate circuit provisioning as an utility max-min fair allocation problem and provide the first solution under multiple paths scenario
• Apply a adaptive load-balance protocol on re-routing
• Conduct empirical study on two backbone networks, Abilene and GEANT
• Show more than 95% of traffic can be carried by the network with carefully static circuit provisioning & all traffic can be routed after re-routing
38
Publication
• Jerry Chou, Bill Lin, "Coarse Optical Circuit Switching by Default, Rerouting over Circuits for Adaptation,“ Journal of Optical Networking, vol. 8, no. 1, pp. 33-50 (2009).
39
Thank You
40
Backup Slides
41
Work-In-Progress
• Capacity planning
• Fault-tolerance
• Better adaptive routing algorithms
• Joint circuit-provisioning and routability optimization
42
Motivation
• Traffic growing nearly twice rate of Moore’s Law– Difficult for electronic packet routers to keep up
• On the other hand, optical switching provides abundance of transmission capacity (e.g. WDM)– Rate of increase in optical transport capacity keeping pace
with traffic growth (with 100 Gbps per wavelength in next generation), well above Moore’s Law
– Rate of decrease in cost per unit of optical transport capacity well below Moore’s Law
43
Networks• Traffic used for prediction (over months)
– Abilene: 03/01/04 - 04/21/04, GEANT: 01/01/05 – 04/10/05
• Optical circuits only change on hourly basis (method to be discussed)
• Simulated actual traffic (over a week)– Abilene: 04/22/04 - 04/26/04, GEANT: 04/11/05 – 04/15/05
• To consider a highly utilized network, we scaled traffic by a factor, such that at least one link is saturated under OSPF.– Abilene: 4, GEANT: 2
44
Questions
• How to decide on circuit provisioning to maximize probability that the circuits provide sufficient capacity to carry traffic?– Formulated as a multi-path utility max-min fair bandwidth
allocation problem
• What to do when circuit capacity is not enough?– Adaptive load-balancing over circuits that have spare
capacity
45
Multi-Path Utility Max-Min Algorithm
• Based on water-filling algorithm and maximum concurrent flow (MCF) solver
1. Determine bandwidth allocation that achieves the maximum common utility for all flows
2. Determine path distribution by MCF routing
3. Identify saturated flows and fix their utility
Max utility
Fill-up by with a routing
Saturated flow
46
Binary Search• Find maximum utility by binary search over [0, 1]
– Determine flow traffic by utility functions– Find feasible route by querying a MCF solver
• If <1, decrease utility, otherwise increase utility
20
2010 30 40 50
4060
80
100
20
2010 30 40 50
4060
80
100
20
2010 30 40 50
4060
80
100
20
2010 30 40 50
4060
80
100
BW BW BW BW
Utility(%
)
Utility(%
)
Utility(%
)
Utility(%
)
C = 100Max utility Traffic
1 (50,50,50,50) 0.5
.0.6 (10,40,10,40) 1
0.5 (10,30,10,40) 1.25
47
Piece-Wise Linear Search
• Approximate utility functions as piecewise linear functions
• Replace binary search by searching through each piecewise linear segment– Query MCF by the inverse of slope as traffic– is proportional to maximum utility
Seg I
Seg II
Seg III
Seg IV
20
2010 30 40 50
406080
100
BW
Utility(%
)
20U[1
] - U[0
]
BW[1]-BW[0]10 20 30 40
0
105.0
10u
48
Identifying Saturated Flows
• By residual capacity is not enough– Miss-identified saturated flow in earlier iteration would
produce smaller bandwidth allocation
A
B
C
D
E
F
Let link capacity = 10
Bandwidth requirement: AE = 5, AF = 5
If select path ACDF, AE is saturated
If select path ABDF, AE is not saturated
49
Identifying Saturated Flows
• A flow is saturated if its utility cannot be increased by any feasible routing
• To guarantee optimality, flows have to be re-routed
50
Multi-Path vs. Single-Path
• Significantly higher utility– Minimum utility 92.90% vs. 74.74%
51
Avoiding Cycles
• Problem: packets may go in circles– Never reach destination– Waste circuit capacity
• One solution is to limit “time-to-live” (TTL)
• Alternatively, ensure “loop-free” routingby routing table construction
52
Loop-Free Routing Tables
• For OD-pair, solve maxflow to derive largest “ayclic” graph on “circuit”
• Build routing tables using both “source” and “destination” prefixes
s
t
53
Current Contributions
• New paradigm of optical circuit switching by default, packet routing when necessary
• First solution to the multi-path utility max-min fair bandwidth allocation problem
• Though not presented, utility max-min fair solver has been applied to a Denial-of-Service network security problem
54
r(C) = 15r(C) = 20r(C) = 25
Localized Adaptive Re-routing
• Basic idea: load-balance on outbound circuits, weighted by spare capacity
r(B) = 30
r(D) = 25
B
C
DA
1. r(B) < B[A, B] ?YES
NO
2. k = random (wk)
Optical Circuit
3535
35
r(B) = 35
Problem1: greedy solution based only one-hop info.Problem2: oscillation of weights could occur
Problem1: greedy solution based only one-hop info.Problem2: oscillation of weights could occur
55
Distributed Adaptive Re-routing
• Basic idea: 1. Collect path info. by a Distance-Vector-like protocol 2. Load-balance outgoing weights based on path cost
s t1
1
4
32
4
1
1
2 1
56
Distributed Adaptive Re-routing
Step1: Compute path cost– Every router measure downstream link cost– Exchange info. by a Distance-Vector-like protocol
s t
cost: 1
1
1
4
32
4
1
1
2 1
cost: 1
57
Distributed Adaptive Re-routing
Step1: Compute path cost– Every router measure downstream link cost– Exchange info. by a Distance-Vector-like protocol
s t
cost: 1+1=2
1
1
4
32
4
1
1
2 1
cost: 4+1=5
cost: 3+1=4
58
Distributed Adaptive Re-routing
Step1: Compute path cost– Every router measure downstream link cost– Exchange info. by a Distance-Vector-like protocol
s t
cost: 2+2=4
1
1
4
32
5
1
1
2 1
If weights are equalCost: (2+4)*0.5 +(5+5)*0.5 = 8