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P2P Media Streaming for Optimal Data Assignment and System Capacity Amplification

This seminar presentation discusses the peer-to-peer media streaming model, self-growing characteristics, optimal media data assignment, and fast system capacity amplification. It also explores the numeric indexing for efficient music data retrieval.

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P2P Media Streaming for Optimal Data Assignment and System Capacity Amplification

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  1. NIBEDITA MAULIKGRAND SEMINAR PRESENTATION OCT 21st 2002

  2. On Peer-to-Peer Media Streaming Purdue University • P2P data streaming • Supplying Peers stream media file to requesting peers • Requesting peers play back and store media data during streaming session • Requesting peers become supplying peers after streaming session • Characteristicsself growing, server less, heterogeneous in outbound • bandwidth contribution, supplying peers/requesting peer relation is many to one • Problems • Media data assignment for a multi-supplier P2P streaming session (Optimal Media Data Assignment) • Fast amplification of P2P streaming capacity(Fast System Capacity Amplification) • Streaming model • Each supplying peer participates in at most one P2P streaming session at any time • Capacity of the P2P system is the total number of P2P streaming sessions simultaneously provided by the system • The in-bound bandwidth of the requesting peer Pr is related to Ro i.e. the playback rate of the media data as • Rin( Pr ) = Ro, • The out-bound bandwidth of the supplying peers Ps has values Ro/2, Ro/4, Ro/8…..Ro/2N classified as class-n peers for the out-bound bandwidth of Ro/2n

  3. OPTIMAL MEDIA DATA ASSIGNMENT Buffering Delay : Time interval between the start of the transmission and the start of the playback at Pr

  4. OPTIMAL MEDIA DATA ASSIGNMENT(Algorithm) • Assignment of first 2n segments is computed ; the assignment repeats itself every 2n segments for the rest of the media file. • OTSp2p is optimal

  5. FAST SYSTEM CAPACITY AMPLIFICATION

  6. FAST SYSTEM CAPACITY AMPLIFICATION(Algorithm) • Distributed Admission Control Protocol DACp2p • Each supplying peer individually decides in a probabilistic fashion with different probability value applied to different classes of requesting peers, whether or not to participate in a streaming session • A requesting peer may send a ‘reminder’ to a busy supplying peer , reminding it not to elevate its admission preferences to requesting peers of classes lower than that of the requesting peer DACp2p – Supplying Peers Ps maintains an admission probability vector <Pr[1], Pr[2], ….Pr[N]> Pr[i] = probability with which a class-I requesting peer with be supplied in case Ps is not busy in another streaming session The vector is computed as follows 1. If Ps is a class-k peer, for 1<=i<=k Pr[i] =1.0 & for k<i <=N Pr[i] = 1/ 2 i-k 2. If Ps has been idle, the probability vector will be updated after Tout. For k<i<N, Pr[i]=Pr[i]*2 3. If Ps just finished a streaming session, it will update it’s probability vector as follows - if during the session it did not receive any request from it’s favored class then K<i<=N Pr[i]=Pr[i]*2 - if it received at least 1 request from it’s favored class, however it was not granted because Ps was busy and the requesting peer left a ‘reminder’ and the class of that requesting peer was L then 1<=i<=L Pr[i]=1.0 and L<i<=N , Pr[i] = 1/2 i-k

  7. FAST SYSTEM CAPACITY AMPLIFICATION(Algorithm) DACp2p – Requesting Peer - Pr obtains a list of M randomly selected candidate supplying peers by some P2P lookup mechanisms. The class of each candidate is also obtained - Pr obtains enough permissions from supplying peers such that 1. They are neither down nor busy 2. The are willing to provide streaming service 3. Their aggregated out-bound bandwidth offered is Rsum = Ro - Pr executes algorithm OTSp2p to compute media data assignment, triggers the participating supplying peers, and the session begins - If Pr cannot get enough permission it leaves ‘reminder to a subset W of the busy candidates. Members of W are selected as follows 1. The candidate currently favors the class of Pr 2. The aggregated out-bound bandwidth offer of the candidates in W is equal to Ro – Rsum - If Pr is admitted, it becomes a supplying peer when the streaming session is over, if Pr is rejected it will backoff for at least Tbkf before making the request again

  8. THE NUMERIC INDEXING FOR MUSIC DATA Chaoyang University of Technology, Taiwan • Goal • Efficient Content based retrieval of audio & music data • To transform the music data into numeric forms and develop an index structure for effective retrieval • Ensure scalability in an efficient audio/music data retrieval system • Suffix tree for Music Indexing • Constructed from a symbol with length m symbols, consists of m leaf nodes numbered from 1 to m • Any two branches from a non-leaf node should be labeled with different symbols • The number of each node points out the start position of the sub-string which consists of the symbols labeled from the root to this leaf node of the tree

  9. NUMERIC INDEXING • Let music data consist of n notes Do, Re, Mi ….etc each note represented by a music symbol ‘a’, ‘b’, ……., ‘n’ respectively • Each music symbol is mapped onto integer values 0, 1, ……., n-1 • For a music segment with m adjacent notes x1x2x3….xm, the integer value of each note is to be represented by P(xi), this segment can be transformed into a numeric value by the function v(m) =  P(xi) X n i-1 • Modified R-tree to construct numeric index structure • Each non-leaf node of the modified R-tree consists of the upper bound and lower bound of numeric values of the sub-tree under it • Each leaf node of the R-tree stores the transformed value of music feature string and a linked list of target music • Each entry in the linked list consist of music information and a pointer • Each music information consist of the music ID in the database and the start position of the music segment in the music • The pointer points to the next entry with the same transformed value

  10. EXAMPLE FOR NUMERIC INDEXING

  11. EXACT MATCHING • The music query segment has to be found the exact same pattern in a melody from the music database • e.g Music query segment ccdbb . Two sub-segments of length 4 will be ccdb & cdbb of value 1322 & 1132 • Leaf node 1322 -> (S2, 2) , (S3, 1) Leaf node 1132 -> (S2, 3) , (S3, 4) • The intersection yields {S2, S3} -> both music feature strings consist of segments ‘ccdb’ & ‘cdbb’ • Check if the starting symbols of sub-segments found in each music feature string are adjacent each other as the sequence of query segment. Here sub-segments ‘ccdb’ and ‘cdbb’ starting on 2nd and 3rd symbols of S2 have the identical sequence as the query. Hence S2 is the target music

  12. APPROXIMATE MATCHING • Transform the music query string into numeric value • Compare the value with the values in the leaf node • Iff the difference between the transformed value and the value of leaf node matches one of the conditions listed in Table 1, there will be a target node found and it will be the target music

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