Topologically-Aware Overlay Construction and Server Selection

1. Topologically-Aware Overlay Construction and Server Selection Sylvia Ratnasamy, Mark Handly, Richard Karp and Scott Shenker Presented by Shreeram Sahasrabudhe (sas4@lehigh.edu) CSE 498 Advanced Networks

2. Motivation • The potential benefit by some knowledge of topology for distributed internet applications • Improving application-level connectivity • Need for solution which is • Simple • Fast – for the dynamic target systems • Distributed – no central point of failure or bottleneck • Scalable – for millions of nodes

3. Approach • Network Measurement used: Network Latency • Non-intrusive • Light-weight • End-to-end • Priorities: (Scalability + Practicality) > Accuracy • Binning Scheme • Evaluation of the application of this scheme to overlay networks and server selection

4. Distributed Binning • Requires a set of landmark machines spread across the internet. (8-12 machines) • Nodes measure RTT to each of these landmarks and orders the landmarks in increasing RTT. (relative) • Divide the range of possible latency values into layers (absolute)

5. Scalable? • Each node only needs measure with small set of landmarks • At a million nodes on the network, refreshing at every hour, each landmark would approximately handle 2700pings/sec. (800Mhz m/c; DNS root servers handle DNS queries at 1600/sec) • Better scalability by have multiple nodes at a location act as a single logical landmark.

6. Sanity Check • For each node in a bin compute Gain ratio = inter-bin latency / intra-bin latency • Ratio = Reduction in Latency = Desirable • Algorithm tested on simulated topologies (Transit Stub, Power-law Random Graphs) and internet data (NLANR). • Improvement rapidly saturates • Gain ratio is affected by the size of the underlying topology

7. Binning Vs (Random, Nearest-Neighbor) • Random Binning: Each node selects a bin at random. • Nearest Neighbor Clustering: At each iteration, two closest clusters are merged into a single cluster.

8. Construction of Overlays • Structured: Nodes are interconnected (at application-level) in a well-defined manner. Content-Addressable Network, Chord, PASTRY, Tapestry • Unstructured: Less structured networks End-system multicast, Scattercast. • Measurement metric is Latency Stretch: ratio of average inter-node latency on the overlay network to the average inter-node latency on the underlying IP-level network. • Latency Stretch = Better!

9. Construction of CAN topologies using Binning • Ordering of landmarks is used for binning • m landmarks, m! orderings • Co-ordinate space divided along first dimension into m portions, each portion sub divided along second dimension into m-1 portions and so on • New node joins CAN at a random portion associated with its landmark ordering • Result • Co-ordinate space not uniformly populated • Uneven distribution of size of zone spaces (future work!)

11. Unstructured Overlays • Given a set of n nodes on the Internet, have each node pick any k neighbor nodes from this set, so that the average routing latency on the resultant overlay is low. • Use a heuristic algorithm: node picks k/2 closest nodes and k/2 at random • Short-Long: Shortest path routing, short-long-short • BinShort: k/2 nodes from self-bin and rest k/2 at random.

12. Server - Selection If (Server=bin[client]) > 1 • Redirect to a random server in same bin Else • Select existing server with most_similar_bin to client’s (Degree of similarity between two bins = no of matches of positions in landmark ordering) Stretch = (latency to selected server) / (latency to optimal server)

13. - Improved performance - diminishing returns - For TS-10K 1000 servers, rest clients - For NLANR data - Adjusted stretch