Presentation is loading. Please wait.

Presentation is loading. Please wait.

Informed Content Delivery Across Adaptive Overlay Networks J. Byers, J. Considine, M. Mitzenmacher and S. Rost Presented by Ananth Rajagopala-Rao.

Similar presentations


Presentation on theme: "Informed Content Delivery Across Adaptive Overlay Networks J. Byers, J. Considine, M. Mitzenmacher and S. Rost Presented by Ananth Rajagopala-Rao."— Presentation transcript:

1 Informed Content Delivery Across Adaptive Overlay Networks J. Byers, J. Considine, M. Mitzenmacher and S. Rost Presented by Ananth Rajagopala-Rao

2 Motivation CDNs typically use overlay multicast and there is tremendous potential to leverage parallel downloads Encoded packets make it easy to coordinate transfers Need efficient algorithms to compute set differences between content stored at peers

3 Motivation (cont.) (a)Tree Topology (b)DAG (c)Fully collaborative scenario

4 The Digital Fountain Approach Gives a large unordered universe of encoded symbols Stateless Encoding – Each packet is generated independent of the previously generated packets Additivity – Parallel downloads from multiple sources with full content requires no orchestration

5 Sparse Parity Check Codes A file is a set of symbols of 1k blocks x i. Each encoded symbol is an XOR of a “random” set of these blocks. If we represent each symbol by a bit- vector of the blocks used to generate it, the rank of the matrix of bit-vectors of the symbols a host has is the # of blocks it can recover.

6 Sparse Parity Check Codes (cont.) Re-encoding is very easy, and XOR of any set of encoded symbols is also a valid symbol. For carefully engineered distributions (taking into account the overhead of encoding), the expected overhead is approximately 3 to 5 percent.

7 Reconciliation of Content Between Peers A – set of symbols that A has B – set of symbols that B has We have to compute A-B (approximately?) If n is the number of symbols needed to reconstruct the file, the interesting numbers are o = |A-B|/|A| r = (n - |B|)/|A-B|

8 Estimating o Use random sampling, send a sample of k elements of A to peer B. Use min-wise summaries (from search engine literature for document similarity estimation). Give a good tradeoff between accuracy and amount of data to send. Trivial to combine min-wise summaries of A and B to get summary of (A U B).

9 Min-wise summaries

10 Exact Approaches (high r and high o) A sends entire set to B - O(|A| log u) Use a hash function, we make the miss- probability arbitrarily low for a packet of size O(|A| log |A|) Very high overhead

11 Bloom Filter Approach (low r and low o) Choose i hash functions {h 1, h 2.. h i } with range [0,m). Construct a bitmap of m bits where for each element x of the set, h j (x) is set for all j in [1,i]. Probability of false positive is f = (1-e -k|A|/m ) k Low bandwidth overhead, high computation overhead of O(|B|).

12 Approximate Reconciliation Trees Low overhead bandwidth. Allows for more efficient computation than bloom filters O((|A-B|+|B-A|)log|B|) Use bloom filters on top of trees similar to Merkle trees. What about the computation overhead at node A??

13 ART (cont.)

14 ART (performance)

15 One full sender and one partial sender

16 Four Partial Senders

17 Conclusions A whole bag of interesting algorithmic tools is presented, but it is not clear what is the best way to combine and use all these tools. Suggests some new and interesting directions of research in P2P, overlay multicast and CDNs.


Download ppt "Informed Content Delivery Across Adaptive Overlay Networks J. Byers, J. Considine, M. Mitzenmacher and S. Rost Presented by Ananth Rajagopala-Rao."

Similar presentations


Ads by Google