breaking the single-path barrier
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Breaking the Single-Path Barrier. Brad Smith Jack Baskin SoE Research Review Day 10/20/2011. Outline. Research Corporate Partnership Open Source Network Lab. The Internet is Single-Path. Compute best path per destination Destination-based (“hop-by-hop”) forwarding 2 problems - PowerPoint PPT PresentationTRANSCRIPT
Breaking the Single-Path Barrier
Brad SmithJack Baskin SoE Research Review Day
10/20/2011
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Outline
• Research• Corporate Partnership• Open Source Network Lab
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The Internet is Single-Path
• Compute best path per destination• Destination-based (“hop-by-hop”) forwarding• 2 problems– Quality-of-Service– Congestion
S
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Problem – Quality-of-Service
• Requires multiple paths per destination• Example – 2 paths
• 2Mbps, 200ms• 100Kbps, 20ms
• Depends on application!– Video streaming– Voice over IP (VoIP)
b/w = 100Kbpslatency = 20ms
b/w = 2Mbpslatency = 200ms
Next hop for D?S
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Problem – Congestion
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Strong tendency for paths to share links…
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Previous Work
• Circuit switch – special path per flow• Solve part of the problem– Congestion – only need a small number (≤ 4)– Minimum delay– Partial solutions - disjoint widest and shortest
• In practice - over-provision a single path• Challenge – what are enough paths?
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Best Set of Paths• Paths as points in multi-dimensional space• Some paths are “better” than others• Best set of paths are those with none better• Paths that provide the full range of performance
100Kbps, 20ms100Kbps, 20ms 100Kbps, 20ms
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Using the Best Set of Paths
• Assign flows to paths that satisfy QoS• In general, there is more than one…• …choose one that minimizes congestion
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100Kbps, 20ms
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Simulations
• Generate random networks• Compute routing tables at all nodes– Total bandwidth– Delay
• Generate random stream of flows• Use oracle to assign flows to paths– Satisfies QoS– Has bandwidth
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Simulations (cont)
• Measure Call Acceptance Ratio– % flow requests successfully routed
• Across range of networks– Size – number of vertices– Connectivity – average degree (# neighbors)
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CAR: 350 Vertices, Degree 32
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CAR: 350 Vertices, Degree 16
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CAR: 350 Vertices, Degree 4
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• Converge when lightly loaded and over-loaded
• Multipath does better in-between
• Fewer resources poorer and less distinct performance
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Target CAR Flow Rate
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95%
600Load supported by given infrastructure & routing architecture.
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95% CAR Rate: Degree 32
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Multipath provides dramatic capacity increase with same infrastructure.
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MP @ 95% CAR Rate – # Vertices
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Solid gains with increasing infrastructure.
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Ratio MP:SP 95% CAR – # Vertices
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4 to 11x gains… with opportunities for improvement(!).
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From 1 to many layers…
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Future Work
• Routing protocols (path computation)– Link state– Distance vector
• Congestion management (path selection)– Routing– Network feedback
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Thank you!