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Optimizing Cost and Optimizing Cost and Performance for Performance for
MultihomingMultihoming
ACM SIGCOMM 2004ACM SIGCOMM 2004
Lili QiuLili QiuMicrosoft ResearchMicrosoft [email protected]@microsoft.com
Joint Work withJoint Work withD. K. Goldenberg, H. Xie, Y. R. Yang, Yale D. K. Goldenberg, H. Xie, Y. R. Yang, Yale
University University Y. Zhang, AT&T Labs – ResearchY. Zhang, AT&T Labs – Research
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Multihoming & Smart RoutingMultihoming & Smart Routing
Multihoming– A popular way of
connecting to Internet
Smart routing– Intelligently distribute
traffic among multiple external links
User
ISP 1
ISP K
InternetISP 2
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Potential BenefitsPotential Benefits• Improve performance
– Potential improvement: 25+% [Akella03]– Similar to overlay routing [Akella04]
• Improve reliability– Two orders of magnitude improvement in
fault tolerance of end-to-end paths [Akella04]
• Reduce cost
Q: How to realize the potential benefits?
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Our GoalsOur Goals• Goal
– Design effective smart routing algorithms to realize the potential benefits of multihoming
• Questions– How to assign traffic to multiple ISPs to
optimize cost? – How to assign traffic to multiple ISPs to
optimize both cost and performance?– What are the global effects of smart
routing?
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Related WorkRelated WorkTechniques for implementing multihoming
– BGP peering, DNS-based, NAT-based (e.g., [RFC2260, Cisco, GCLC04, Radware, F5])
– Complementary to our workPerformance evaluation [Akella03,Akella04]
– Quantify the potential benefits of multihoming– Unaddressed challenge: how to achieve this in
practiceSmart routing
– Commercial products (e.g., [RouteScience, Internap, Proficient, …])
– Technical details are unavailableHash-based load balancing [Cao01, Guo04]
– Optimizes neither performance nor cost
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Network ModelNetwork Model• Network performance metric
– Latency (also an indicator for reliability)– Extend to alternative metrics
• log (1/(1-lossRate)), or latency+w*log(1/(1-lossRate))
• ISP charging models– Cost = C0 + C(x)– C0: a fixed subscription cost– C: a piece-wise linear non-decreasing function
mapping x to cost – x: charging volume
• Total volume based charging• Percentile-based charging (95-th percentile)
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Percentile Based ChargingPercentile Based Charging
Interval
Sorted volume
N95%*N
Charging volume: traffic in the (95%*N)-th sorted interval
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Why cost optimization?Why cost optimization?• A simple example:
– A user subscribes to 4 ISPs, whose latency is uniformly distributed
– In every interval, the user generates one unit of traffic
• To optimize performance– ISP 1: 1, 0, 0, 0, …– ISP 2: 0, 1, 0, 0, …– ISP 3: 0, 0, 1, 0, …– ISP 4: 0, 0, 0, 1, …– 95th-percentile = 1 for all 4 ISPs– 95th-percentile = 1 using one ISP
• Cost(4 ISPs) = 4 * cost(1 ISP)
Optimizing performance alone could result in high cost!
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Cost Optimization: Cost Optimization: Problem Specification (2 ISPs)Problem Specification (2 ISPs)
Time
Volume
N1 2
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Cost Optimization: Cost Optimization: Problem Specification (2 ISPs)Problem Specification (2 ISPs)
Time
Volume
P1
P2
Goal: minimize total cost = C1(P1)+C2(P2)
Sorted volume
Sorted volume
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Issues & InsightsIssues & Insights• Challenge: traditional optimization techniques
do not work with percentiles
• Key: determine each ISP’s charging volume
• Results– Let V0 denote the sum of all ISPs’ charging volume – Theorem 1: Minimize cost Minimize V0
– Theorem 2: V0 ≥ 1- k=1..N(1-qk) quantile of original traffic, where qk is ISP k’s charging percentile
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Cost Optimization: Cost Optimization: Problem Specification (2 ISPs)Problem Specification (2 ISPs)
Time
Volume
P1
P2P1 + P2 90-th percentile of original traffic
Sorted volume
Sorted volume
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Intuition for 2-ISP CaseIntuition for 2-ISP Case• ISP 1 has 5% intervals whose traffic exceeds
P1• ISP 2 has 5% intervals whose traffic exceeds
P2
• The original traffic (ISP 1 + ISP 2 traffic) has 10% intervals whose traffic exceeds P1+P2
• P1+P2 90-th percentile of original traffic
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Sketch of Our AlgorithmSketch of Our Algorithm1. Determine charging volume for each ISP
– Compute V0
– Find pk that minimize ∑k ck(pk) subject to ∑kpk=V0 using dynamic programming
2. Assign traffic given charging volumes– Non-peak assignment: ISP k is assigned pk
– Peak assignment: • First let every ISP k serve its charging volume pk
• Dump all the remaining traffic to an ISP k that has bursted for fewer than (1-qk)*N intervals
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Additional IssuesAdditional Issues• Deal with capacity constraints
• Perform integral assignment– Similar to bin packing (greedy heuristic)
• Make it online– Traffic prediction
• Exponential weighted moving average (EWMA)– Accommodate prediction errors
• Update V0 conservatively• Add margins when computing charging
volumes
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Optimizing Cost + PerformanceOptimizing Cost + Performance• One possible approach: design a metric
that is a weighted sum of cost and performance– How to determine relative weights?
• Our approach: optimize performance under cost constraints– Use cost optimization to derive upper
bounds of traffic that can be assigned to each ISP
– Assign traffic to optimize performance subject to the upper bounds
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Evaluation MethodologyEvaluation Methodology• Traffic traces (Oct. 2003 – Jan. 2004)
– Abilene traces (NetFlow data on Internet2)• RedHat, NASA/GSFC, NOAA Silver Springs Lab,
NSF, National Library of Medicine• Univ. of Wisconsin, Univ. of Oregon, UCLA, MIT
– MSNBC Web access logs
• Realistic cost functions [Feb. 2002 Blind RFP]
• Delay traces– NLANR traces: 3 months’ RTT measurements
between pairs of 140 universities– Map delay traces to hosts in traffic traces
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Baseline AlgorithmsBaseline Algorithms1. Round robin
– In each interval, assign traffic to a single ISP– Rotate in a round robin fashion
2. Equal split– In each interval, split traffic equally among ISPs– Similar to hash-based load balancing
3. Offline local fractional– Minimize the total cost for each interval
independently
4. Dedicated links– Flat rate and independent of usage
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Cost Comparison for Different TracesCost Comparison for Different Traces
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Red Hat MIT UCLA Wisconsin Web Server
No
rmal
ized
co
st t
offline cost online cost round robin equal LFA offline
Our algorithms significantly out-perform the alternatives.
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Cost Comparison for Varying # Links Cost Comparison for Varying # Links
For all # ISPs, our cost optimization performs well.
0
1
2
3
4
5
6
7
8
0 5 10 15
# external links
No
rmalize
d c
ostl
offline cost online cost round robin
equal LFA offline
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Cost + Performance EvaluationCost + Performance Evaluation
Optimizing performance alone often doubles the cost.
0
0.5
1
1.5
2
2.5
3
Co
st n
orm
aliz
ed b
y o
ffli
ne
cost
offline cost offline cost+perf online cost
online cost+perf offline perf
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Cost + Performance Evaluation Cost + Performance Evaluation (Cont.)(Cont.)
Our dual metric optimization achieves low cost and latency.
0
10
20
30
40
50
60
70
Avera
ge L
ate
ncy (
ms)
offline cost offline cost+perf online cost online cost+perf offline perf
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Global Effects of Smart RoutingGlobal Effects of Smart Routing• Selfish nature of smart routing
– Each user optimizes its own cost & performance without considering its impact on other traffic
– Need to understand its global effects
• Questions– How well does smart routing perform when traffic
assignment affects link latency?– How well do different smart routing users co-exist?– How well do smart routing users co-exist with
single-homed users?
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Evaluation MethodologyEvaluation Methodology• Abilene traffic traces• Rocketfuel inter-domain topology
– 170 nodes, 600 edges– With propagation delay and OSPF weights – M/M/1 queuing model
• Routing– A user selects best performing ISP subject to
cost constraints– Inter-domain: shortest AS hop count– Intra-domain: OSPF
• Compute traffic equilibria as in [QYZS03]
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Global Effects: SummaryGlobal Effects: Summary• Impact of self interference is small
• Smart routing users co-exist well with each other
• Smart routing users co-exist well with single-homed users
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ConclusionsConclusionsContributions
– First paper on jointly optimizing cost and performance for multihoming
– Propose a series of novel smart routing algorithms that achieve both low cost and good performance
– Under traffic equilibria, smart routing improves performance without hurting other traffic
Future work– Further evaluation through Internet experiments– Dynamics of interactions among different users– Design better charging models
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Thank you!
