Tapestry: Finding Nearby Objects in Peer-to-Peer Networks Joint with: Ling Huang Anthony Joseph Robert Krauthgamer John Kubiatowicz Satish Rao Sean Rhea Jeremy Stribling Ben Zhao Object Location Behind the Cloud Why nearby? (DHT vs. DOLR) Nearby= low stretch, ratio of distance traveled to
find object to distance to closest copy of object Objects are services, so distance isnt one-time cost (see COMPASS) (smart) publishers put objects at chosen locations in network Bob Miller places retreat schedule at node in Berkeley Wildly popular objects Well-Placed Objects Popular Objects Outline Low stretch dynamic peer-to-peer network Tolerate failures in network Adapting to network variation Future work
Distributed Hash Tables System Neighbor Motivating s Structure Hops CAN, 2001 O(r) grid O(rn1/r) Chord, 2001
1 Big Challenge: Joining Nodes Theorem 1 [HKRZ02] When peer A is finished inserting, it knows about all relevant peers that have finished insertion. Results Correctness O(log n) insert & delete Concurrent inserts in a lock-free fashion Neighbor-search routine Required to keep low stretch All low-stretch schemes do something like this Zhao, Huang, Stribling, Rhea, Joseph & Kubiatowicz (JSAC) This works! Implemented algorithms
Measured performance Neighbor Search In growth-restricted networks (with no additional space!): Theorem 2 [HKRZ02] Can find nearest neighbor with high probability with O(log2 n) messages Theorem 3 [HKMR04] Can find nearest neighbor, and messages is O(log n) with high probability Outline Low stretch dynamic peer-to-peer network Tolerate failures in network Adapting to network variation Future work
Behind the Cloud Again Dealing with faults Multiple paths Castro et. al One failure along path, path breaks Wide path Paths faulty at the same place to break Exponential difference in width effect retrofit Tapestry to do latter in slightly malicious networks Failed!
Still good Effective even for small overhead 100 90 % failed routes 80 1 2 3 4 5 6 70
60 50 40 30 20 10 0 0.1 0.2 0.3 0.4 0.5 Fraction of Bad Nodes
Theorem 4 In growth restricted spaces, can make probability of failed route less than 1/nc for width O(clog n) Hildrum & Kubiatowicz, DISC02 Wide path vs. multiple paths 90 80 4 4 Single Failed Paths 70 60 50 40 30 20
10 0 0 0.1 0.2 0.3 Fraction of Bad nodes 0.4 0.5 0.6 Outline
Low stretch dynamic peer-to-peer network Tolerate failures in network Adapting to Network Variation Future work Digit size affects performance 600 500 Work 400 300 200
100 0 0 5 10 15 Base 20 25 Network not homogeneous Previous schemes picked a digit size How do we find a good one?
But what if there isnt one? Nebraska Paris San Francisco New Result Pick digit size based on local measurements Dont need to guess Vary digit size depending on location No, its not obvious that this works, but it does! Hildrum, Krauthgamer & Kubiatowicz [SPAA04]: Dynamic, locally optimal low-stretch network
Conclusions and Future Work Conclusion Low stretch object location is practical System provably good [HKRZ02] System built [ZHSJK] Open Questions Do we need a DOLR? Object placement schemes? Workload? Examples where low stretch, load balance, and low storage not possible simultaneously What is tradeoff between degree, stretch, load balance as function of graph? Can we get best possible? Trade off smoothly? Tapestry People
Ling Huang Anthony Joseph John Kubiatowicz Sean Rhea Jeremy Stribling Ben Zhao andOceanStore group members
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