1 Geographic Information Infrastructures for Ubiquitous Computing Spring 2007 Ki-Joune Li.
Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan...
-
Upload
phebe-norman -
Category
Documents
-
view
217 -
download
0
Transcript of Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan...
![Page 1: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/1.jpg)
Massively Distributed Database Systems
Broadcasting - Data on airSpring 2014Ki-Joune Li
http://isel.cs.pusan.ac.kr/~likPusan National University
![Page 2: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/2.jpg)
2
Why Broadcasting?
• Simple• Data Access Pattern: mostly asymmetric • Scalability – Very adequate for massively distrib-
uted environments• Example• DMB• TPEG
![Page 3: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/3.jpg)
3
TPEG – Transport Protocol Experts Group
• Broadcasting traffic information protocol
![Page 4: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/4.jpg)
4
TPEG – Message format
![Page 5: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/5.jpg)
5
TPEG Service Contents Example
![Page 6: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/6.jpg)
6
TPEG Service
![Page 7: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/7.jpg)
7
Air Update – Map Data Update
![Page 8: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/8.jpg)
8
Basic Idea – Broadcast DisksDisk Broadcast
Disk Access Time Frequency (Broadcasting Period)
Block Packet
Memory Hierarchy Multiple Broadcasting Disks (paper -1)
File Structure Message Format (paper -2)
Indexing Indexing Broadcasting (paper – 3)
Query Processing Query processing for Broadcasting Data(paper – 4)
![Page 9: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/9.jpg)
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 Sys-
tems”, Wireless Network, Vol.10, pp.723-736, 2004• tisa.org, TPEG, http://www.tisa.org/assets/Uploads/
Public/TISA14001TPEGWhatisitallabout2014.pdf
![Page 10: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/10.jpg)
10
Paper #1 – Broadcasting disks in SIGMOD 1995
![Page 11: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/11.jpg)
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
![Page 12: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/12.jpg)
12
Message Format
• Given three data items A, B, and C to broadcast with different access probability,
Flat format
Skewed format
Multiple disks format
![Page 13: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/13.jpg)
13
Performance Measures
• What is the goal?• To minimize the average waiting time (expected delay)
• Example
![Page 14: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/14.jpg)
14
Message Formatting Method - Server
• 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
Relative frequenciesF(T1)=1, F(T2)=2, F(T3)=4
LCM=4 minor cycles
Length(T3)/LCM=2
Major Cycle=S*LCM
![Page 15: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/15.jpg)
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
![Page 16: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/16.jpg)
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
![Page 17: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/17.jpg)
17
Paper #2 – Organization and Access, TKDE 9(3), 1997
![Page 18: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/18.jpg)
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 re-
quests data to the point of completing data downloads • Tuning time + Latency Data Access Time
![Page 19: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/19.jpg)
19
Broadcast data format
Bucket ID
Bcast ptr
idx ptr
Bucket type
Bucket
. . .
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
![Page 20: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/20.jpg)
20
Data Access
. . .
1. Client joins here
Index
2. Wait until the index arrives
3. Wait until data bucket arrives
. . .
4. Read data
![Page 21: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/21.jpg)
21
Where to place Index
No Index
Single Index
(1,m) Index
What’s the difference? Probably (1,m) may improve the performance
![Page 22: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/22.jpg)
22
How to organize
• Full duplication vs. Relevant Duplication
![Page 23: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/23.jpg)
23
No replication
![Page 24: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/24.jpg)
24
Entire Path Replication
![Page 25: Massively Distributed Database Systems Broadcasting - Data on air Spring 2014 Ki-Joune Li lik Pusan National University.](https://reader030.fdocuments.net/reader030/viewer/2022032803/56649e395503460f94b2aafd/html5/thumbnails/25.jpg)
25
Distributed Index