1. Opportunistic Traffic Scheduling Over Multiple Network Path Coskun Cetinkaya and Edward Knightly

2. Edward Knightly Multi-Path Routing Establishes and simultaneously uses multiple parallel paths –Key advantage is efficiency Routing protocol assigns weights to paths –OSPF, QoS routing, traffic engineering

3. Edward Knightly Existing Splitting Techniques Per packet round robin forwarding –Simplest and most frequently used –Degrades TCP throughput due to re-ordering Per flow hashing –Fine splitting granularity and no TCP re-ordering –Per-TCP-flow lookup limits implementation feasibility Destination prefix based forwarding –Coarse-granularity splitting and no TCP re-ordering –Unpredictable load splitting that may not match desired weights All ignore path quality in splitting decision

4. Edward Knightly Our Thesis Observe –Routing weights change slowly (from traffic engineering) –Quality of paths changes continuously Opportunistic Multipath Scheduling –Exploits short-term capacity variations on different paths via scheduling packets to opportunistically favor low-delay paths –Obey weights at long time scales to ensure “global” objectives Hypothesis –Improve throughput/delay, no per-flow lookup, satisfy weights –TCP throughput improvements due to RTT reduction will overwhelm re-ordering effects

5. Edward Knightly System Model Design: scheduling/traffic splitting policy Objective: minimize mean delay of multipath traffic –Decrease RTT and loss rate  increase TCP throughput Subject to: mean traffic on path i =  i (path weight) Multipath traffic … Cross traffic Splitter

6. Edward Knightly X k = size of packet k I(s k,i) = 1 of packet k is scheduled on path i, 0 otherwise For equal capacity paths minimizing delay is equivalent to minimizing the expected queue length Mathematical Formulation

7. Edward Knightly Optimal Scheduler Assumptions: –Cross-traffic and multi-path traffic are stationary processes  queue length is stationary –Multi-path traffic does not change path conditions Using a wireless scheduling analogy [LCS02], we can show that the optimal scheduler is threshold based: Contrast to “join the shortest queue” policy which ignores weights

8. Edward Knightly Evaluate the performance under self-similar cross traffic Queue size distribution is Weibull: Expected queue size (and delay): Round RobinOptimal Scheduler Performance of the Optimal Policy

9. Edward Knightly On-Line Computation of v* In practice, we do not know the queue length or its distribution Threshold update: –stochastic approximation technique [KC78,LCS02] Scheduling decision: –Q i k estimated via probes

10. Edward Knightly Evaluation Scenario Two paths with capacity 10 Mb/sec Cross-traffic: self-similar with mean rate m  [0.3, 0.9], variance coefficient a  [0.5,4], and Hurst Parameter H  [0.5,0.9] Multi-path traffic is constant-rate or TCP Gain defined as …

11. Edward Knightly Model: gain depends only on H and # paths and is  50% Higher N  more path diversity  higher gain Large H  long-time scale path correlation  higher gain Homogeneous Paths: Model

12. Edward Knightly Simulated gains higher than predicted by model –Model serves as lower bound –Queue distribution is asymptotic lower bound, tighter for larger queues Delay increases with increasing mean (m) and variance coefficient (a) Gain (relative) is highest under higher H, lower m, lower a Homogeneous Paths: Simulation

13. Edward Knightly Gain increases with path diversity (increasing ratio of variance coefficient) –OMS exploits different path properties subject to weights Heterogeneous Paths: Impact of Variance Coefficient Ratio H=0.6 m=0.7

14. Edward Knightly So far, assumed path information is immediately available at scheduler/splitter RTT-scale delay to obtain buffer state (via probes or ECN) Gain decreases as information delay increases High gain for measured values of traffic (0.7 < H < 0.85) and delay (1 < RTT < 100 msec) Effect of Information Delay m=0.9 a=0.5

15. Edward Knightly When can OMS do worse than RR? Three combined factors: –iid traffic having no long-time-scale bursts –High information delay –High ratio of multi-path traffic to cross-traffic (scheduled traffic itself determines conditions) Limits of OMS

16. Edward Knightly TCP Multi-Path Traffic With RR, multipath traffic achieves only 20% to 38% of fair share –High cost of mis-ordering and delay TCP/OMS significantly outperforms TCP/RR –TCP/OMS requires an aggregate level of only 10 cross-traffic flows to achieve maximum performance –OMS impact overwhelms effect of TCP variants 10 msec probing interval 32 kb/s probing overhead (0.32% of capacity)

17. Edward Knightly Probing Interval and TCP Traffic Base case probing interval: 10 msec interval and 32 kb/sec Faster 1 msec probing yields higher-than-fair share for multi-path flows Slower probing (e.g., 3.2 kb/sec) reduces performance

18. Edward Knightly Summary Multipath routing promises increased efficiency and performance Today’s traffic splitting ignores path dynamics and –inhibits TCP throughput via reordering, –requires expensive per-TCP flow lookups, or –cannot achieve weights via prefix splitting Opportunistic Multipath Scheduling –Improves throughput/delay via a measurement based opportunistic policy that satisfies routing weights –Gains overwhelm occasional misordering http://www.ece.rice.edu/networks