Massively Distributed Database Systems

Broadcasting - Data on airSpring 2014Ki-Joune Li

http://isel.cs.pusan.ac.kr/~likPusan National University

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Why Broadcasting?

• Simple• Data Access Pattern: mostly asymmetric • Scalability – Very adequate for massively distrib-

uted environments• Example• DMB• TPEG

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TPEG – Transport Protocol Experts Group

• Broadcasting traffic information protocol

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TPEG – Message format

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TPEG Service Contents Example

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TPEG Service

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Air Update – Map Data Update

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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)

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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

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Paper #1 – Broadcasting disks in SIGMOD 1995

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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

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Message Format

• Given three data items A, B, and C to broadcast with different access probability,

Flat format

Skewed format

Multiple disks format

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Performance Measures

• What is the goal?• To minimize the average waiting time (expected delay)

• Example

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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

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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

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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

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

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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

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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

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Data Access

. . .

1. Client joins here

Index

2. Wait until the index arrives

3. Wait until data bucket arrives

. . .

4. Read data

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Where to place Index

No Index

Single Index

(1,m) Index

What’s the difference? Probably (1,m) may improve the performance

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How to organize

• Full duplication vs. Relevant Duplication

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No replication

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Entire Path Replication

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Distributed Index