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Overlays and DHTs Presented by Dong Wang and Farhana Ashraf Schedule Review of Overlay and DHT RON Pastry Kelips Review on Overlay and DHT Overlay Network build on top of another network Nodes connected to each other by logical/virtual links Improve Internet Routing and Easy to deploy P2P(Gnutella, Chord…), RON DHT Allows you to do lookup, insert, delete objects with keys in a distributed settings Performance Concerns Load Balancing Fault Tolerance Efficiency of lookups and inserts Locality CAN, Chord, Pasrty, Tapestry are all DHTs Resilient Overlay Network Acknowledged: http://nms.csail.mit.edu/ron/ Previous CS525 Courses David G. Andersen, etc. MIT SOSP 2001 RON-Resilient Overlay Network Motivation Goal Design Evaluation Discussion RON-Motivation Current Internet Backbone NOT be able to • Detect failed path and recover quickly • BGP takes several mins to recover from faults • Detect flooding and congestion effectively • Leverage redundant path efficiently • Express fine-grained policy/metrics RON-Basic Idea B A D C Inequality of Triangles does not usually hold for Internet! -Eg. Latency-It is possible that: AB+BC<AC RON makes use of underlying path redundancy of Internet to provide better path and route around failure RON is end to end solution, packets are simply wrapped around and sent normally RON-Main Goals Fast Failure Detection and Recovery Tighter integration of routing/path selection with the application Average detect and recover delay<20s Application can specify metrics to affect routing Expressive Policy Routing Fine-grained and aim at users and hosts RON-Design Overlay Old idea in networks Easily deployed and let Internet focus on scalability Only keep functionality between active peers Approach Aggressively probe all inter-RON node paths Exchange routing information Route along best path (from end to end view) consistent with routing policy RON-Design: Architecture Probe between nodes, detect path quality Store path qualities at Performance Database Link-state routing protocol among nodes Data are handled by application-specific conduit and forwarded in UDP RON-Design: Routing and Lookup Policy routing Metric optimization Classify by policy Generate table per policy Application tags the packet with its specific metric Generate table per metric Multi-level routing table and 3 stage lookup Policy->Metric->Next hop RON Design-Probing and Outage Detect Probe every PROBE_INTERVAL (12s) With 3 packets, both participants get an RTT and reachability without syn. Clocks If probe is lost, send next immediately, up to 3 more probes (PROBE_TIMEOUT 3s) Notify outage after 4 consecutive probe loses Outage detection time on average=19 s RON Design-Policy Routing Allow user to define types of traffic allowed on particular network links Place policy tags on packets and build up policy based routing table Two policy mechanisms exclusive cliques general policies RON-Evaluation Two main dataset from Internet deployment RON1-N=12 nodes, 132 distinct paths, traverse 36 AS and 74 Inter-AS paths RON2-N=16 nodes, 240 distinct paths, traverse 50 AS and 118 Inter-AS paths Policy-prohibit sending traffic to or from commercial sites over the Internet2 RON Evaluation-Major Results Increase the resilience of the overlay network RON takes ~10s to route around failure Compared to BGP’s several minutes Many Internet outage are avoidable Improve performance –Loss rate, Latency, TCP Throughput Single-hop indirect routing works well Overhead is reasonable and acceptable RON vs Internet 30 minute loss rates For RON1-able to route around all outages; For RON2-about 60% outages are overcome Performance-Loss rate Performance-Latency Performance-TCP Throughput Performance Improvement: RON employs Appspecific metric optimization to select path Scalability Conclusions RON improves network reliability and performance Overlay approach is attractive for resiliency: development, fewer nodes, simple substrate Single-hop indirection in RON works well RON also introduces more probing and updating traffic into network RON-Discussion Aggressiveness RON never back-off as TCP does Can it coexist with current traffic on Internet? What happens if everyone starts to use RON? Is it possible to modify RON to achieve good behavior in a global sense Scalability Trade scalability for improved reliability Many RONS coexisting in the Internet Hierarchical structure of RON network Distributed Hash Table Problem Route a msg with key, K to the node, Z which has a ID closest to key K Not scalable if routing table contains all the nodes d471f1 d467c4 d462ba X=d46a1c d4213f Tradeoffs Memory per node Lookup latency Number of messages d13da3 Route(d46a1c) A = 65a1fc Pastry: Scalable, decentralized object location and routing for large-scale peer-to-peer systems Antony Rowstron Peter Druschel Middleware 2001 Acknowledged: Previous CS525 Courses Motivation Node IDs are assigned randomly With high probability nodes with adjacent IDs are diverse Considers network locality Seeks to minimize distance messages travel Scalar proximity metric #IP routing hops RTT Pastry: Node Soft State Storage requirement in each node = O(log N) Immediate Neighbors in ID space (Used for routing) Used for routing Nodes closest according to locality (Used to update routing table) Similar to successor and predecessor Similar to finger table entries Pastry: Node Soft State Leaf Set Neighborhood Set Contains L nodes, closest in the ID space Contains M nodes, closest according to proximity metric Routing Table Entries of row n, shares exactly the first n digits with the local node Nodes are chosen according to proximity metric Pastry: Routing Case I Key within leaf set Case II (Prefix Routing) Key not within leaf set Route to the node in the leaf set with ID closest to key Route a node in the routing table, such that the new node shares one more digit with the key than the local node Case III Key not within leaf set Case II not possible Route to a node which shares at least same number of digits with the key, but is closer to the key than the local node Routing Example d471f1 d467c4 d46a1c Cuts the ID space into 1/(2^b) Number of hops needed is log2^bN d462ba d4213f lookup(d46a1c) 65a1fc d13da3 Self Organization: Node Join d471f1 Z=d467c4 X=d46a1c d462ba d4213f New node: X=d46a1c A is X’s neighbor Route(d46a1c) A = 65a1fc d13da3 Pastry: Node State Initialization New node: X=d46a1c d471f1 Leaf set (X) = leaf set (Z) Neighborhood set (A) = neighborhood set (X) Routing Table Z=d467c4 d462ba X=d46a1c d4213f Route(d46a1c) A = 65a1fc B= d13da3 Row zero of X = row zero of A Row one of X = row one of B Pastry: Node State Update X informs any nodes that need to be aware of its arrival X also improves its table locality by requesting neighborhood sets from all nodes X knows In practice: optimistic approach Pastry: Node departure (failure) Leaf set repair (eager – all the time): Routing table repair (lazy – upon failure): Leaf set members exchange keep-alive messages Request set from furthest live node in set Get table from peers in the same row, if not found – from higher rows Neighborhood set repair (eager) Routing Performance Average no. of hops = log(N) Pastry uses locality information Kelips: Building an Efficient and Stable P2P DHT through Increased Memory and Background Overhead Indranil Gupta, Ken Birman, Prakash Linga, Al Demers, and Robert van Renesse Acknowledged: Previous CS525 Courses IPTPS 2003 Motivation For n=1000000 and 20 byte per entry Storage requirement at a Pastry node = 120 byte Not using memory efficiently How can we achieve O(1) lookup latency? Increase memory usage per node Design Consists of k virtual affinity groups Each node member of an affinity group Soft State Affinity Group View Contacts (constant sized) set of nodes lying in each of the foreign affinity groups Filetuples (Partial) set of other nodes lying in the same affinity group (partial) set of filename and host IP address (homenode), where homenode lies in the same affinity group Contains RTT, heartbeat count for each of the entries Kelips: Node Soft State soft state affinity group view 15 76 18 id hbeat rttime … 18 1890 23ms 167 2067 67ms contacts 160 group 129 167 0 contactnodes [129, 30, … ] 1 [15, 160, …] filetuple 30 fname … Affinity Group # 0 #1 p2p.txt … # k-1 homenode [18, 167, … ] Storage Requirement @ Kelips Node S(k,n) = n/k Affinity groups c * (k-1) + F/k entries Contacts Minimized at k = √( (n+F) / c ) + Assume F is proportional to n, and c fixed Optimal k = O(sqrt(n)) For n=1000000, and F = 10 million Total memory requirement < 20 MB Filetuples Algorithm: Lookup Lookup (key D at node A) Affinity group G of homenode of D = hash(D) A sends message to closest node X in the contact set for affinity group G X finds homenode of D from filetuple set O(1) lookup Maintaining Soft State Heartbeat mechanism Soft state entries refreshed periodically within and across groups Each node periodically selects a few nodes as gossip targets and sends them partial soft state information Uses constant gossip message size O(log n) complexity for gossiping Load Balance N = 1500 with 38 affinity groups 1000 nodes with 30 affinity groups Discussion Points What happens when triangular inequality does not hold for a proximity metric? What happens for high churn rate in Pastry and Kelips? What was the intuition behind affinity groups? Can we use Kelips in a Internet scale network? Conclusion Chord A DHT tradeoff: Storage requirement Lookup Latency Pastry Kelips Storage O(log N) O(log N) requirement O(√n) Lookup Latency O(1) O(log N) O(log N) Going one step further One hop lookups for p2p overlays http://www.usenix.org/events/hotos03/tech/talks/gupta_talk.pdf