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Broadcast data dissemination&IR-Approach

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Information

Dissemination

Goal : Maximize query capacity of servers, minimize energy per query at the client.

Focus: Read-only transactions (queries).– Clients send update data to server – Server resolves update conflicts, commits updates

1. Pull: PDAs demand, servers respond. backchannel (uplink) is used to request data and provide

feedback. poor match for asymmetric communication.

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Mobile Computing Environments

Wired network

Fixed Host Fixed HostFixed Host

Fixed Host

MSS

Mobile Client

Mobile Client MSS

Mobile Client

Mobile Client

One mobile server provides service for many mobile clients.“Data broadcasting approach” is proposed to efficiently disseminate data to a large number of mobile clients at the same time.

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Model of data broadcasting

A broadcast server broadcasts a file consisting of a number of records periodically on a broadcast channel.

A mobile client tunes in to the channel and waits for a required data to arrive.

A B C D E F G H

Broadcast Server Mobile Client

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Information Dissemination…

2. Push: Network servers broadcast data, PDA's listen. PDA energy saved by needing receive mode only. scales to any number of clients. data are selected based on profiles and registration in each

cell.

ServerClients

A B CD

G F E

. .

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Information Dissemination…

ServerClients

A B CD

G F E

. . 14.4 Kbps

3. Combinations Push and Pull (Sharing the channel). Selective Broadcast: Servers broadcast "hot" information only.

"publication group" and "on-demand" group. (Push) On-demand request:

Non-hot data are requested by mobile client on other channel. (Pull)

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Broadcast Data Dissemination

Miami island sight-seeing data broadcasting stock related data traffic information Microsoft SPOT

Stands for smart personal objects technology proposed to use FM radio subcarrier frequencies

for disseminating public and private information.

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Organization of Broadcast data

Flat: broadcast the union of the requested data cyclic.

Skewed (Random): broadcast different items with different frequencies. goal is that the inter-arrival time between two

instances of the same item matches the clients' needs.

A B C

A A B C

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Broadcast Disks

Multi-Disks Organization [Acharya et. al, SIGMOD95]

The frequency of broadcasting each item depends on its access probability.

Data broadcast with the same frequency are viewed as belonging to the same disk.

Multiple disks of different sizes and speeds are superimposed on the broadcast medium.

No variant in the inter-arrival time of each item.

B C

ADisk1

Disk2A B A C

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Selective

Tuning

Basic broadcast access is sequential Want to minimize client's access time and tuning time.

active mode power is 250mW, in doze mode 50μW What about using database access methods? Hashing: broadcast hashing parameters h(K) Indexing: index needs to be broadcast too

"self-addressable cache on the air"

(+) "listening/tuning time" decreases

(-) "access time" increases

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Structure of a broadcast program

There are two type of buckets in a broadcast program Index nodes , Data item Index nodes are interleaved between data items in the

broadcast schedule.

A B C FED indexindex A B C

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廣播環境下的資料擷取方式 ― 使用索引擷取資料 (Tuning_opt)

index d1 d2 d3 d4 d5 d6 d7

使用索引 (Tuning_opt) 可以減少客戶端待在活動模式的時間，但會增加獲取資料的平均等待時間

index d1 …….

休眠

客戶端切入時間點

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b1 b2b3 b4

b5b6 b7 b8

b9

a1 a2 a3

R

1 4 7 10 13 16 19 22 25 28 31 34

c1 c2 c6c5c4c3 c7 c8 c9 c10 c11 c12 c13 c27c26c25c24c23c22c21c20c19c18c17c16

c15c14

37 40 43 46 49 52 55 58 61 64 67 70 73 76 79~81

Traverse the index tree from Root to a data item(d10).

R a1 b2 c4 item 10

●降低取得索引的成本、 c層的一半 (14 個 bucket) vs. 索引路徑 (4 個bucket)

廣播環境下的資料擷取方式 ―多階層索引

index

data

(1,28,54)

(28,37,46) (55,64,73)(1,10,19)

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Indexing

(1,M) Indexing: We broadcast the index M times during one version of the data.

All buckets have the offset to the beginning of the next index segment.

Distributed Indexing Cuts down on the replication of index material Divides the index into:

– replicated top L levels, non-replicated bottom 4-L levels

Flexible Indexing Broadcast divided into p data segments with sorted data. A binary control index is used to determine the data segment A local index to locate the specific item within the segment

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(1,m) Indexing

There are m index segments in a broadcast cycle. Each index segment consists all the indices in the index tree. (1,m) indexing scheme reduces the probability that a mobile client misses the

indices when probes in. The main weakness is that the replicated index buckets increases the size of a

broadcast cycle, which increases the access time.

Full Index Partial Data Full Index Partial Data

R a1 …. c26 c27 1 2 3 4 5 6 7 8R a1 …. c26 c27

…. Partial Data

…. 81

A broadcast cycle

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EPR 廣播排程（ distributed indexing ）

R

A1 A2

B1 B2 B3 B4

1 2 3 4 5 7 86

Replicated Part

Non-Replicated PartNon-replicated Root

1 2R A1 B1 3 4B2R A1 5 6A2 B3R 8B4R A2 7

Broadcast R to B1, and all children of B1

●重複播放索引、減少錯過索引的機率

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Caching in

Broadcasting

Data are cache to improve access time Lessen the dependency on the server's choice of broadcast priority Traditionally, clients cache their "hottest" data to improve hit ratio Cache data based on PIX: Probability of access (P)/Broadcast frequency (X). Cost-based data replacement is not practical:

requires perfect knowledge of access probabilities comparison of PIX values with all resident pages

Alternative: LIX, LRU with broadcast frequency pages are placed on lists based on their frequency (X) lists are ordered based on L, the running avg. of interaccess

times page with lowest LIX = L/X is replaced

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Prefetching in Broadcasting

Client prefetch page in anticipation of future accesses No additional load to the server and network Prefetching instead of waiting for page miss can reduce the cost of a

miss PT prefetching heuristic [Archarya et al. 96]

- pt: Access Probability (P) * period (T) before page appears next- A broadcast page b replaces the cached page c with lowest pt

value Team tag - Teletext approach [Ammar 87]

Each page is associated with a set of pages most likely to be requested next

When p is requested, D (D:cache size) associated pages are prefetched

Prefetching stops when client submit a new request

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Cache

Invalidation Techniques

When? Synchronous: send invalidation reports periodically Asynchronous: send invalidation information for an item as

soon as its value changes; E.g., Bit Sequences [Jing 95] To whom?

Stateful server: to affected clients Stateless server: broadcast to everyone

What? invalidation: only which items were updated propagation: the values of updated items are sent aggregated information/ materialized views

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Synchronous

Invalidation

Stateless servers are assumed. Types of client: Workalcholic and sleepers [Barbara Sigmod 94] Strategies:

Amnestic Terminals: broadcast only the identifiers of the items that changed since the last invalidation report

abort T, if x є RS(T) appears in the invalidation report Timestamp Strategy: broadcast the timestamps of the latest

updates for items that have occurred in the last w seconds.

abort T, if ts(x) > tso(T) Signature Strategy: broadcast signatures.

A signature is a compressed checksum similar to the one used for file comparison.

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IR架構

客戶端用專屬頻道下載資料 為了效率，客戶端會快取 (Cache)使用過的資料 伺服器端會異動資料 伺服器端廣播驗證 ( 異動 ) 報告 (Invalidation Report)

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IR的操作方法

客戶端欲使用快取中的資料項A ，但 A 的內容是否與伺服器

端的一致 ?

1. 取得 IR

2. 以 IR驗證快取中的資料，刪除過時的資料

3. 若 A 不存在快取中，透過專屬頻道向伺服器端取得 A

server

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IR包含的資訊

伺服器端每隔 L 時間廣播一個 IR，毎個 IR會含有過去 W*L這段時間異動的的資訊。

在 IR中，每筆異動訊息包含兩個欄位資料項的 ID和該資料項被異動的時間(timestamp) 。

IR包含過去 W*L時段的異動資訊： client端可錯失 IR，而本方法仍然有效。

IR IRIRL L

W * L

Ti

IR IRL L

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Example - 製作 IR

Example : W = 3 L =10

IR IRIR

a b ac b a

1

6

a

b

ID Ts 17

6

a

b

ID Ts

13c

27

25

a

b

ID Ts

13c

IR的內容

10 20 301 6 13 17 2527

Sever端

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Example – client如何使用 IR驗證

2.client在 15上線 query資料項 b ， client cache中有存資料項b

，資料項 b 的 timestamp=10，上次收到 IR的時間 10。

IR

10 20

c a

Client query資料項 b

Example 1 (快取有效） :

(10>20-3*10) ，未離線過久；以 IR刪除過時資料

17

10

a

b

ID Ts

13c

10b

ID Tsclient的cache上次收到 IR的時間10

cache中有資料項b ，以 cache資料項 b 回答 query b

20b

ID Ts

client的cache

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Example – client如何使用 IR驗證

3. client在 24上線 query資料 b ， client cache中有存 資料項 b ， timestamp=20，上次收到 IR的時間 20。

IR

20 30

b a

Client上線query資料項 b

Example 2（快取無效） :

20b

ID Tsclient的cache上次收到 IR的時間20

27

25

a

b

ID Ts

13c

cache中沒有資料項 b ，向 Server發 request取得資料項 b 並儲存於cache之中，回答 query

30b

ID Ts

client的cache

(10>20-3*10) ，未離線過久；以 IR刪除過時資料

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存取 IR的流程

Ti: 最新收到 IR的時間。 Td: 離線前最後一次收到 IR的時間。 (dc,tc) : dc client cache裡的資料 id， tc 資料的異動時間 。 (dx,tx) : dx出現在 IR的資料 id， tx 出現在 IR的資料的異動時間 。

Td > Ti - WLclient收到 IR

No

刪除快取中所有的資料項

Yestc < tx

Yes將 dc 從 cache中刪除

留下資料 dc ， tc=Ti

No

快取中的資料 ID和 IR中的資料 ID相同(dc=dx)