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Optimizations for Locality-Aware Structured Peer-to-Peer Overlays

Optimizations for Locality-Aware Structured Peer-to-Peer Overlays. Jeremy Stribling strib@mit.edu Collaborators : Kris Hildrum John D. Kubiatowicz The First IRIS Student Workshop August 10, 2003. Berkeley. Rice. Object Location in Tapestry. MIT. Berkeley. Rice.

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Optimizations for Locality-Aware Structured Peer-to-Peer Overlays

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  1. Optimizations for Locality-Aware Structured Peer-to-Peer Overlays Jeremy Stribling strib@mit.edu Collaborators: Kris Hildrum John D. Kubiatowicz The First IRIS Student Workshop August 10, 2003

  2. Berkeley Rice Object Location in Tapestry MIT IRIS Student Workshop – 8/10/03

  3. Berkeley Rice Is This Always Optimal? MIT IRIS Student Workshop – 8/10/03

  4. Discussion • Why is this a problem? • Latency, efficiency, availability • Metric: Relative Delay Penalty (RDP) • Distance through Tapestry vs. IP distance • Solution: trade storage for low local area RDP • Will work in DOLRs with a pointer indirection layer IRIS Student Workshop – 8/10/03

  5. Rice Optimization 1: Backups • Redundancy: Store up to c nodes in each entry • c–1 nodes are backups • A simple optimization: publish to b backups • Limit to first h hops of publish path • Result • Nodes near the object more likely to encounter pointers • Cost: b*h additional pointers per object IRIS Student Workshop – 8/10/03

  6. Optimization 1: Backups Experiments run in simulation on a GT-ITM transit stub topology IRIS Student Workshop – 8/10/03

  7. Rice Optimization 2: Nearest Neighbors • Observation: In Opt. 1, choice for backups is limited • But lots of nodes at each level, many may be nearby • Optimization: publish to n nearest neighbors • Limit to first h hops of the publish path • Result • If n is large, essentially local area flooding • Analytical cost: n*h additional pointers per object IRIS Student Workshop – 8/10/03

  8. Optimization 2: Nearest Neighbors Experiments run in simulation on a GT-ITM transit-stub topology IRIS Student Workshop – 8/10/03

  9. Rice Optimization 3: Local Surrogate MIT Berkeley IRIS Student Workshop – 8/10/03

  10. <= x*t ms > x*t ms x ms Optimization 3: Local Surrogate • Solution: Check local node before leaving • When publishing, place a pointer on local surrogate • Occurs naturally on Coral, LAND, SkipNet • In practice, storage cost is very low • Issue: What determines a wide area hop? • One metric: if next hop is more than t times longer than last hop, consider it wide area IRIS Student Workshop – 8/10/03

  11. Optimization 3: Local Surrogate Experiments run in simulation on a GT-ITM transit-stub topology IRIS Student Workshop – 8/10/03

  12. Future Work • Automatically adjust t when using local surrogate • Take measurements on actual networks • Test optimizations with real workloads • Evaluate the maintenance cost Questions? IRIS Student Workshop – 8/10/03

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