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Dynamics of End-host controlled

Routing

Mukund SeshadriProf. Randy Katz

Sahara Retreat Jan 2004

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Problem Consider multiple independent overlay

networks/flows, each choosing the best overlay route Can this be unstable/inefficient?

Identify such scenarios. Suggest improvements.

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Motivation Overlay routing can provide better

functionality, performance or resilience. e.g. RON[3], Detour[4], ESM[5].

What if several entities set up their own overlay flows?

e.g. using overlay support primitives [2].

Primary app – multimedia streams. Flows can have some physical links in

common, no explicit coordination. e.g. on popular shared test-beds like PlanetLab[6].

Different networks/independent flows from same network.

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More Background Resilient Overlay Networks

Recovers from routing failures in around 20s, as opposed to several minutes in normal BGP.

If default route from Node A to B fails, then data is redirected through Node C.

All available paths are probed frequently Does not scale beyond 50 nodes

End System Multicast End-hosts form a low-delay or low-b/w degree-

bounded mesh and then a multicast tree.

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Sources

Destinations

1+ Mbps(L2)

1 Mbps(L3)

Sources

Destinations

Sources

Destinations

Sources

Destinations

Sources

Destinations

Sources

Destinations

Unstable Routing Example

Data Paths

Available Paths

BottleneckPhysical Link

Overlay Nodes

Oscillation of both flows between the two alternate paths is possible.

Each source has a 1Mbps flow.

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Outline of Study Used simulations to study requirements for

good performance and factors affecting it. Some form of “restraint” is needed

Hysteresis Threshold (H) Randomized selection Decision times.

Automatic discovery of H Factors affecting performance

Size and number of flows, path density, cross-traffic, more…

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Simulation Model M overlay networks/flows with N available overlay paths each

All paths monitored Available b/w inferred “perfectly” in a time window (Tm)

Configurable error factor Best path is selected to send traffic on (GREEDY) Route change based on bandwidth improvement threshold (H)

Periodic decisions (Tr) M: 100-1000, N: 5-50.

Path-level simulator Characterizes shared bottleneck links. The level of sharing is characterized by “path density” (Pf) Unicast CBR flows with bandwidth requirement. Flows arrive and depart with lifetimes around 1000 sec.

Metric: Loss Rate (related to bandwidth).

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Simulation Parameters Unless mentioned otherwise, these are the values used for

system parameters.

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Need for Hysteresis No/Low hysteresis => very unstable, high loss : red line. H too high => high loss due to poor route selection :

blue line. Optimal value of H : green line.

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Path density and H The best value of H varies significantly with

path density (Pf), flow size and other parameters.

The minimum of each each line is the best setting of H, for that value of Pf .

High Pf => greater chance of interaction => worse stability and loss rate.

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Other factors and H The best value of H varies

with other parameters too:

Relative flow size – proportional to inter-arrival-time (IAT).

Cross-traffic percentage.

Explanation of observed trends: the impact of a flow’s re-routing is more significant when a it is a larger fraction of link capacity.

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Other factors - Summary Combination of following factors

leads to poor performance High path density Large flow size and number Low cross-traffic High load High variation in bandwidths.

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Routing window (Tr) and H Increasing routing window

while keeping measurement window constant can improve performance

Since the no. of flows re-routing during the measurement window decreases.

But can increase reaction time after failure.

Best value of H (line minimum) varies a lot…

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Improvements to GREEDY Randomly select path to be chosen

ARAND: In proportion to available bandwidths SRAND: Best of randomly selected subset of

size S …in proportion to capacity Reduces measurement overhead Works well for server load balancing [1]

(different work model: jobs are assigned to only one server for their lifetime)

GRAND: Randomly select from the best S paths

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Randomized Selection Much lower loss than

GREEDY SRAND and ARAND best

Best value of H still varies w.r.t. path density, etc, for SRAND

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Automatic Discovery of H We propose that flows automatically discover

the most suitable values of H. Flows can independently “probe” values of H

No route change => decrease H Route change => increase H

MIMD works slightly better than other methods.

High initial value; “quick-start” decrease phase (high decrease factor)

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Performance of H-discovery

Very low loss-rates compared to fixed-H.

•Upper edge of C.I. is much lower than GREEDY.

• H-discovery works well in all scenarios, including high IAT, below.

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“Cheating” flows What if most flows use “restrained” selection,

while some “cheat” by using more aggressive GREEDY methods?

When “good” flows use fixed H The cheaters obtain much lower loss rates Good flows don’t suffer unless cheaters exceed 35%

of all flows. When good flows use H-discovery

The cheaters do not benefit Good flows’ loss increases when cheaters exceed

20% of all flows, but the loss is still lower than with fixed-H.

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“Cheating” flows - Graphs The cheaters benefit when “good” flows use fixed

H …but not with the H-discovery method

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Conclusion Already summarized the effect of different

factors on performance. Restraint is useful in route selection.

H, randomization, Tr

We propose dynamic discovery of H Low loss rate in all scenarios.

Future work Investigate dynamic models of flow and cross-

traffic. Study the usefulness of these forms restraint in

network-layer routing.

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References1. How Useful is Old Information – M.Mitzenmacher –

PODC 19972. Infrastructure Primitives for Overlay Networks –

Karthik Lakshminarayanan et al. – under submission.3. Resilient Overlay Networks – Andersen et al – SOSP

20014. Detour: a Case for Informed Routing and Transport –

Savage et al. – IEEE Micro Jan 1999.5. A Case for End System Multicast – Yang-hua Chu et al.

– JSAC 2002.6. PlanetLab – http://www.planet-lab.org

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