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					    Optimizing Cost and
Performance for Multihoming
                      Lili Qiu
                Microsoft Research
                 liliq@microsoft.com

                  Joint Work with
D. K. Goldenberg, H. Xie, Y. R. Yang, Yale University
          Y. Zhang, AT&T Labs – Research

               ACM SIGCOMM 2004
       Multihoming & Smart Routing
                            Multihoming
        ISP 1
                              – A popular way of
                                connecting to Internet

                 Internet
User    ISP 2               Smart routing
                              – Intelligently distribute
                                traffic among multiple
                                external links
         ISP K


                                                       2
            Potential 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?
                                                   3
                  Our 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?


                                                    4
                  Related Work
Techniques for implementing multihoming
   – BGP peering, DNS-based, NAT-based
     (e.g., [RFC2260, Cisco, GCLC04, Radware, F5])
   – Complementary to our work
Performance evaluation [Akella03,Akella04]
   – Quantify the potential benefits of multihoming
   – Unaddressed challenge: how to achieve this in practice
Smart routing
   – Commercial products
     (e.g., [RouteScience, Internap, Proficient, …])
   – Technical details are unavailable
Hash-based load balancing [Cao01, Guo04]
   – Optimizes neither performance nor cost


                                                              5
                Network 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)             6
         Percentile Based Charging

           Sorted volume




                           95%*N         N     Interval


Charging volume: traffic in the (95%*N)-th sorted interval
                                                             7
             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!        8
            Cost Optimization:
      Problem Specification (2 ISPs)
 Volume




                        Time
1 2                 N



                                       9
          Cost Optimization:
    Problem Specification (2 ISPs)
                           Sorted volume
Volume




                                    P1
                           Sorted volume



                    Time
                                    P2
Goal: minimize total cost = C1(P1)+C2(P2)   10
               Issues & 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


                                                            11
          Cost Optimization:
    Problem Specification (2 ISPs)
                                 Sorted volume
Volume




                                          P1
                                 Sorted volume



                         Time

                                          P2
P1 + P2  90-th percentile of original traffic   12
         Intuition 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




                                                       13
          Sketch of Our Algorithm
1. 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
                                                           14
            Additional 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


                                                     15
   Optimizing 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

                                                 16
         Evaluation 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
                                                        17
                Baseline Algorithms
1.       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                   18
  Cost Comparison for Different Traces
                      1.6

                      1.4

                      1.2
  Normalized cost t




                       1

                      0.8

                      0.6

                      0.4

                      0.2

                       0
                            Red Hat         MIT           UCLA     Wisconsin Web Server

                             offline cost   online cost   round robin   equal   LFA offline

Our algorithms significantly out-perform the alternatives.                                    19
                   Cost Comparison for Varying # Links
                   8
                   7
Normalized costl




                   6
                   5
                   4
                   3
                   2
                   1
                   0
                       0                 5                    10                 15

                                             # external links
                              offline cost      online cost        round robin
                              equal             LFA offline

                       For all # ISPs, our cost optimization performs well.           20
Cost normalized by offline cost   Cost + Performance Evaluation
                                   3
                                  2.5
                                   2
                                  1.5
                                   1
                                  0.5
                                   0




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                                                  offline cost        offline cost+perf     online cost
                                                  online cost+perf    offline perf

         Optimizing performance alone often doubles the cost.                                                  21
   Cost + Performance Evaluation (Cont.)
                          70
   Average Latency (ms)


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   offline cost                     offline cost+perf   online cost     online cost+perf   offline perf



Our dual metric optimization achieves low cost and latency.                                               22
   Global 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?

                                                            23
       Evaluation 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]
                                                 24
       Global 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



                                            25
                  Conclusions
Contributions
  – 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                        26
Thank you!

             27

				
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