Massively Distributed Database Systems Broadcasting - Data on air

1. Massively Distributed Database SystemsBroadcasting - Data on air Spring 2014 Ki-Joune Li http://isel.cs.pusan.ac.kr/~lik Pusan National University

2. Why Broadcasting? • Simple • Data Access Pattern: mostly asymmetric • Scalability – Very adequate for massively distributed environments • Example • DMB • TPEG

3. TPEG – Transport Protocol Experts Group Broadcasting traffic information protocol

4. TPEG – Message format

5. TPEG Service Contents Example

6. TPEG Service

7. Air Update – Map Data Update

8. Basic Idea – Broadcast Disks

9. Key papers and documents S. Acharya, et al. “Broadcast Disks: Data Management for Asymmetric Communication Environments”, ACM SIGMOD 1996, pp.199-210 T. Imielinkski, S. Viswanathan, and B.R. Badrinath,“Data on Air: Organization and Access”, IEEE TKDE Vol.9 No.3, 1997, pp.353-372 J. Xu et al. “Energy Efficient Indexing for Quering Location Dependent Data in Mobile Broadcasting Environments, ICDE 2003, pp.239-250 B. Zheng et al. “Spatial Queries in Wireless Broadcast Systems”, Wireless Network, Vol.10, pp.723-736, 2004 tisa.org, TPEG, http://www.tisa.org/assets/Uploads/Public/TISA14001TPEGWhatisitallabout2014.pdf

10. Paper #1 – Broadcasting disks in SIGMOD 1995

11. Key Ideas • Broadcasting as a disk • How to organize broadcast message • Flat Message as a disk • Message with different frequencies as multiple disks • Two Issues • How to organize message – Server Side • How to maintain cache – Client Side

12. Message Format Flat format Skewed format Multiple disks format Given three data items A, B, and C to broadcast with different access probability,

13. Performance Measures • What is the goal? • To minimize the average waiting time (expected delay) • Example

14. Message Formatting Method - Server Relative frequencies F(T1)=1, F(T2)=2, F(T3)=4 LCM=4 minor cycles Major Cycle=S*LCM Length(T3)/LCM=2 • Algorithm • 1. Sort and classify pages by access probability • 2. Determine relative frequency of each disk (page) • 3. Partition each disk into a set of chunks • 4. Define the message format with multiple disks • Example • 4 pages/cycle

15. Caching Policy at Client • Replacement Policy • Not LRU • Point 1Caching hottest page – problematic.If a page is considered as a hottest page by server, then frequent broadcasting, and therefore caching is not really necessary • Point 2Server’s policy is to minimize the average delay!= Local Demands

16. Caching Policy at Client • For a given item A, we need to consider • Broadcasting frequency (X) and • Local access probability (P) • Replacement in terms of • PIX (P/X) instead of LRU

17. Paper #2 – Organization and Access, TKDE 9(3), 1997

18. Key Ideas • Disk Access – Disk Access Time • Two different measures • Latency and • Energy Consumption • Data Access Time in Data on Air • Tuning Time: Amount of time spent by a client listening to the channel  Power Consumption • Latency: Time elapsed from the time that a client requests data to the point of completing data downloads • Tuning time + Latency  Data Access Time

19. Broadcast data format Bucket Bucket ID idxptr . . . Bcastptr Bucket type bcast • Without Index, we need a full scanning of a bcast • Issue • How to organize and Where to place Index • For reducing tuning time and latency

20. Data Access Index 4. Read data . . . . . . 3. Wait until data bucket arrives 2. Wait until the index arrives 1. Client joins here

21. Where to place Index No Index Single Index (1,m) Index  What’s the difference? Probably (1,m) may improve the performance

22. How to organize Full duplication vs. Relevant Duplication

23. No replication

24. Entire Path Replication

25. Distributed Index