Resilient Overlay Networks (RON). Work by: andersen , balakrishnan , Kaashoek , and Morris Appeared In: SOSP Oct 2001 Presented By: Matt Trower and Mark Overholt Some Images Are Taken From Original Presentation. Background. Overlay network: Network built on top of a network
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Resilient Overlay Networks (RON)
Work by: andersen, balakrishnan, Kaashoek, and Morris
Appeared In: SOSP Oct 2001
Presented By: Matt Trower and Mark Overholt
Some Images Are Taken From Original Presentation
Anderson started ISP in Utah before going to MIT
Use inherent diversity of paths to provide link redundancy
Use failure detectors for small subset of nodes in network
Select best path from multiple options
Best Path ≠ Direct Path
C++ Application level library (send/recv interface)
Application-specific routing tables
Policy routing module
Acceptable for small cliques
Done on 3-node system
Ack’s take different path
How does geographic distribution affect RON?
Why doesn’t BGP do this already?
What if everyone was part of a RON?
What if all latencies fall below 20ms?
A Scalable Content-Addressable Network
Work by: Ratnasamy, Francis, Handley, and Karp
Appeared In: SIGCOMM ‘01
A Decentralized system that provides key-value pair lookup service across a distributed system.
DHTs support Insert, Lookup, and Deletion of Data
Image from Wikipedia
CAN is a design for a distributed hash table.
CAN was one of the original four DHT proposals. It was introduced concurrently with Chord, Pastry, and Tapestry
P2P Applications were the forefront in designing CAN.
P2P Apps were scalable in their file transfers, but not very scalable in their indexing of content.
Originally designed as a scalable index for P2P content.
Use of CAN was not limited to P2P apps however. Could also be used in large scale storage systems, content distribution systems, or DNS.
The Overlay Network is a Cartesian Coordinate Space on a d-torus (The coordinate system wraps around).
Each node of the CAN is assigned a “Zone” of the d-dimensional space to manage.
Each Node only has knowledge of nodes in Neighboring Zones.
Assume for now, that each zone has only 1 node.
Zone 1 knows about Zones 4, 5, and 3
Zone 2 knows about Zones 4, 5, and 3
Zone 3 knows about Zones 5, 1, and 2
Zone 4 knows about Zones 2 and 1
Zone 5 knows about Zones 1, 2, and 3
Given a (Key,Value) Pair, hash the Key d different ways, where d is the # of Dimensions
The resulting coordinate is mapped onto the Overlay.
The node responisible for that coordinate is the node that stores the (Key,Value) pair.
A routing message hops from node to node,
Getting closer and closer to the Destination.
A node only knows about its immediate Neighbors
Routing Path Length is (d/4)(n1/d)
As d approaches log(n), the total
Path length goes to log(n).
A new node, N inquires at a Bootstrap node for the IP of any node in the system, S.
Pick a random point, P, in the coordinate space, managed by Node D.
Using CAN Routing, route from S to D.
Node D splits its Zone and gives half to N to manage.
Update the Neighbor List in all Neighboring Nodes to D and N, including D and N.
The Zone is split between the new
Node, N and the old node D.
Node, N, routes to the zone
containing Point P
Need to repair the routing in case of a leaving node or a dead node.
If one of the neighboring zones can merge with the empty zone and maintain Rectilinear Integrity, it does so.
If not, the neighboring Node with the smallest zone attempts to Takeover the zone of the dead node.
Each node independently sends a “Takeover” message to its neighbors.
If a node receives a Takeover message, it cancels its timer if the sending zone is smaller, or it sends a takeover message of its own if it is bigger.
Multi-Dimensioned Coordinate Spaces
Multiple Realities: Multiple Overlapping Coordinate Spaces
Better Routing Metrics
Multiple nodes per Zone
Multiple Hash Functions (replication)
Geographically sensitive overlay
Comparing Basic CAN to “Knobs on Full” CAN
Using some of the improvement made CAN a very robust routing and storage protocol.
Using geographic location in the overlay creation would create smarter hops between close nodes. (But what about a geographically centralized disaster?)
Not much work on Load-Balancing the Keys
When all of the Extra Features are running at once, CAN becomes quite complicated.
Tough to guarantee uniform distribution of keys with hash functions on a large scale.
Work by: Rowstron and Druschel
Appeared In: Middleware ‘01
Maintain overlay network for both arrivals and failures
Network proximity sensitive routing
Per-node state O(logN)
Network proximity-based routing
Owner of obj
Search widens as prefix match becomes longer!
b: tradeoff between local storage and average hop count
L: resiliency of routing
Choose next hop for routes randomly amongst choices
Replicate data to nearby nodes
What does bigger leafset gain you?
How do we decide proximity?
What other features might we want to create a lookup table based upon?