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Routing Dynamics in Simultaneous Overlay Networks. Mukund Seshadri Randy Katz ( mukunds@cs.berkeley.edu randy@cs.berkeley.edu ) Berkeley-Helsinki Short Course Aug. 2003. Problem. Consider overlay routing when multiple independent overlay networks/flows interact:

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routing dynamics in simultaneous overlay networks

Routing Dynamics in Simultaneous Overlay Networks

Mukund Seshadri Randy Katz

(mukunds@cs.berkeley.edurandy@cs.berkeley.edu)

Berkeley-Helsinki Short Course Aug. 2003

problem
Problem
  • Consider overlay routing when multiple independent overlay networks/flows interact:
    • Can this be unstable/inefficient?
      • Identify such scenarios.
      • Suggest improvements.
    • Identify scope for reduction of measurement overhead.
general motivation
General Motivation
  • End-host controlled routing can become significant
    • Pure Overlay Network protocols (RON[3], Detour[4], ESM[5])
    • Overlay primitives (“Path reflection”[1], i3-based [2])
    • Better routing than Internet/BGP (resilience/performance/multicast/etc.)
  • What if several entities set up their own overlays?
    • Companies setting up distribution overlay networks…
    • Or, more ad-hoc users setting up overlay networks…
    • Flows within a single overlay…
  • Consider overlay networks/flows which have some physical links in common, but don’t explicitly coordinate with each other.
unstable routing example
Unstable Routing Example

1+ Mbps

(L2)

Primary Paths

Alternate Paths

2 Mbps

L1

Sources

Bottleneck

Phy. Link

Destinations

1 Mbps

(L3)

Ov.Nw. Nodes

(2 Ovns)

  • L1 failure can cause synchronized oscillation of both flows between the two alternate paths
focus
Focus
  • Main application – multimedia streams
    • Long-lived (medium) flows : ~ 1hr (5min) .
    • Flows require specified bandwidth levels
    • Flows require route stability (Packet-reordering, jitter undesirable)
    • Secondary app – long high volume transfers/sessions
    • Problem considered: selection of best routes (not location/DHTs)
  • Size: 50-500 overlay flows; 10-50 nodes each.
  • Independent decision makers - no explicit info. sharing
    • Unlike PlanetLab[6], underlay[7] model, i3-based soln.[2]
    • Independent administration might be desirable.
    • Don’t have to wait for infrastructure nodes to come up.
    • Most protocols like ESM can’t scale to thousands of nodes.
overlay network model
Overlay Network Model
  • Given M overlay networks/flows with N nodes each
    • Probing of all potential paths is done (O(N) cost).
    • Path characteristics are inferred from probes in some time window
      • With some error factor
      • We consider only bandwidth
    • Best path is selected to send traffic on (GREEDY)
      • Route change based on bandwidth improvement threshold (H)
  • Path-level simulator
    • Characterizes shared bottleneck links.
    • The level of sharing is characterized by “path density”
    • Unicast CBR flows with bandwidth requirement.
  • Metrics of interest
      • Loss Rate (related to bandwidth)
      • Stabilization time
contribution
Contribution
  • Study the need for “restraint” in route selection
      • Randomness in selection selection
      • Hysteresis
      • Time between re-route decisions
hysteresis required
Hysteresis Required
  • No hysteresis threshold (H) for route change => unstable.
  • We will use 99% stabilization time.
h affects loss rate
H affects loss rate…
  • Will explore more later in the talk…
when does greedy fail
When does Greedy “fail”?
  • Defaults:
  • 500 overlay flows,
  • 50 bottleneck links
  • link capacities ~ flow requirements
  • ~50% cross-traffic
  • 10% measurement error.
  • 4x variation in link b/w.
  • ~25 links/flow (density)
  • Optimal Threshold Assumed
  • Large flows => more effect when re-routed => lower stability
when does greedy fail1
When does Greedy “fail”?
  • High sharing=>many route-changes
    • Flows within a single overlay.
    • when overlay nodes are skewed towards certain ASes, like univ.s.
    • if several overlay flows independently use a medium size shared infrastructure.
cross traffic
Cross-Traffic
  • High Cross-Traffic causes the effect of overlay flows on available bandwidths to be lower, so greedy is more stable.
  • Other factors investigated: routing window variation, measurement error, excess capacity, bandwidth distribution.
summary of greedy
Summary of “Greedy”
  • The following factors contribute to poor stability and performance of “Greedy” overlay path selection
    • Several flows’ paths share a large number of bottleneck links.
    • There is not much spare capacity in paths used.
    • There is a large variation in link and flow bandwidths.
    • The overlay traffic is a high fraction of traffic on the bottleneck links
    • Each flow’s bandwidth is significant compared to bottleneck link bandwidth.
improvements to greedy
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 [8]
        • (but different work model: jobs arrive and leave, and are assigned to only one server for their lifetime)
    • GRAND: Randomly select from the best S paths
does randomizing help
Does Randomizing Help?
  • Randomization more useful at high densities.
  • More stable, lower loss, less sensitive to threshold setting.
hysteresis threshold
Hysteresis Threshold
  • Optimal value of H very sensitive to parameters.
  • Flows can automatically discover the values of H.
  • Flows can independently “probe” values of H
    • No route change => decrease H
    • Route change => increase H
  • Try AIAD, MIMD, etc.
  • Can perform even better than with fixed H…
exploring h
Exploring “H”
  • Very similar, MIMD stabilizes slightly quicker…
  • I/D pmtrs. not as sensitive to simulated network pmtrs. as H.
exploring h contd
Exploring “H” (Contd.)
  • Performs much better than with fixed threshold, loss rates close to 0
  • Stabilization times similar to fixed case.
summary
Summary
  • SRAND is as good as or better than GREEDY in most cases
    • Measurement costs lowered, with performance similar to the proportional randomization method.
  • Automatic discovery of H works better than fixed H (and is more feasible).
  • Increasing time windows can help, particularly when flows arrive/depart.
future work
Future Work
  • Define a general method that combines randomization, hysteresis estimation, and time variation (like simulated annealing)
  • Explore dynamic scenarios (flows arrive/depart).
  • Explore 2nd level control loop for MIMD pmtrs.
  • Implement/simulate using real topologies.
  • Can we define a general notion of “friendliness” pertaining to both route selection and traffic distribution over different routes?
references
References
  • Network layer Support for Overlay Networks – John Jannotti – OpenArch 2002.
  • Infrastructure Primitives for Overlay Networks – Karthik Lakshminarayanan et al. – under submission.
  • Resilient Overlay Networks – Andersen et al – SOSP 2001
  • Detour: a Case for Informed Routing and Transport – Savage et al. – IEEE Micro Jan 1999.
  • A Case for End System Multicast – Yang-hua Chu et al. – JSAC 2002.
  • PlanetLab – http://www.planet-lab.org
  • A Routing Underlay for Overlay Networks – Nakao et al. – Sigcomm 2003.
  • How Useful is Old Information – M.Mitzenmacher – PODC 1997
  • An Analysis of Internet Content Delivery Systems – Saroiu et al. – OSDI 2002.
stabilization times of the rands
Stabilization Times of the *RANDs
  • Generally SRAND and ARAND stabilize quickly and have a very low loss rate.
  • Also investigated the effect of subset size on SRAND
other factors
Other Factors
  • Small amount of cheating doesn’t hurt the good flows, large amount does.
  • If link bandwidths are much higher than flow bandwidths, Greedy is more stable and performs better.
  • If link and flow BW are similar, then a high variation in the same causes Greedy to be fairly unstable.
extra slide 2 flow illustration
Extra Slide2-Flow Illustration
  • We can randomize
    • Route selection
      • Proportional to Available BW
    • Time intervals
      • Of assessment and rerouting.