1 mobile data management sanjay kumar madria department of computer science university of...

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Mobile Data Management

Sanjay Kumar Madria

Department of Computer Science

University of Missouri-Rolla

Rolla, MO 65401

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Wireless Technologies Wireless local area networks

(WaveLan, Aironet – Possible Transmission error

Cellular wireless – Low bandwidth Packet radio (Metricom) -Low

Bandwidth Satellites (Inmarsat, Iridium) – Long

Latency

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Mobility Constraints CPU Power Bandwidth Delay tolerance Physical size Constraints on peripherals and

GUIs (modality of interaction) Locations change dynamically

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Why Mobile Data Mgmt?

Wireless Connectivity and use of PDA’s ,

handheld computing devices on the rise Workforces will carry extracts of corporate

databases with them Need central database repositories to serve

these work groups and keep them fairly

upto-date and consistent

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Applications Sales Force Automation - especially in

pharmaceutical industry, consumer goods,

parts Financial Consulting and Planning Insurance and Claim Processing - Auto,

General, and Life Insurance Real Estate/Property Management,

Maintenance and Building Contracting

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Data Processing Scenario One server or many servers (corporate data, inventory, HR, orders/billing) Shared Data Some Local Data per client , mostly subset of global data Need for accurate, up-to-date informationLimitations Short connect time per session Infrequent connections Clients may remain dormant for extended periods

of time Clients not reachable from servers at all times

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What is Mobility?

A device that moves– Between different geographical locations– Between different networks

A person who moves– Between different geographical locations– Between different networks– Between different communication devices– Between different applications

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Device mobility Plug in laptop at home/work on Ethernet

– Occasional long breaks in network access– Wired network access only (connected => well-

connected)– Network address changes– Only one type of network interface– May want access to information when no network is

available: hoard information locally Cell phone with access to cellular network

– Continuous connectivity– Phone # remains the same (high-level network

address)– Network performance may vary from place to place

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Device mobility….

Can we achieve best of both worlds?– Continuous connectivity of wireless access– Performance of better networks when available

Laptop moves between Ethernet, WaveLAN and Metricom networks– Wired and wireless network access– Potentially continuous connectivity, but may be

breaks in service– Network address changes– Radically different network performance on different

networks

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People mobility Phone available at home or at work

– Multiple phone numbers to reach me– Breaks in my reachability when I’m not in

Cell phone– Only one number to reach me– Continuously reachable– Sometimes poor quality and expensive connectivity

Cell phone, networked PDA, etc.– Multiple numbers/addresses for best quality

connection– Continuous reachability– Best choice of address may depend on sender’s

device or message content

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Mobility means changes

How does it affect the following? Hardware

– Lighter– More robust– Lower power

Wireless communication– Can’t tune for stationary access

Network protocols– Name changes– Delay changes– Error rate changes

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Changes…... Fidelity

– High fidelity may not be possible Data consistency

– Strong consistency no longer possible Location/transparency awareness

– Transparency not always desirable Names/addresses

– Names of endpoints may change Security

– Lighter-weight algorithms– Endpoint authentication harder– Devices more vulnerable

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Changes…... Performance

– Network, CPU all constrained– Delay and delay variability

Operating systems– New resources to track and manage: energy

Applications– Name changes– Changes in connectivity– Changes in quality of resources

People– Introduces new complexities, failures, devices

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Example changes Addresses

– Phone numbers, IP addresses Network performance

– Bandwidth, delay, bit error rates, cost, connectivity Network interfaces

– PPP, eth0, strip Between applications

– Different interfaces over phone & laptop Within applications

– Loss of bandwidth triggers change from color to B&W

Available resources– Files, printers, displays, power, even routing

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Most RDBMS vendors support the ISDB scenario - but no design and optimization aids Specialized Environments for ISDB apps:

Sybase Remote ServerSynchrologic iMOBILEMicrosoft SQL server - mobile app supportOracle LiteXtnd-Connect-Server (Extended

Technologies)Scoutware (Riverbed Technologies)

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Personal Communication System Personal Communication System (PCS)(PCS)

Wireless Components

BS

MSC (MTSO)

MS Wirelesscomponent

MS

Cell

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Mobile cellsMetropolitan area Metropolitan area

Coverage area in one cell Coverage area in three cells

BS

BSBSBase Station

Large cells.Low density

Small cells.High density

Smaller cells.Higher density

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Mobile cells

The entire coverage area is a group of a number of

cells. The size of cell depends upon the power of

the base stations.

PSTNMSC

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Frequency reuse

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7

54

3

2

61

7

54

3

2

61

7

54

3

2

D A

AA

AA

AA

NR

D3

D = distance between cells using the same frequencyR = cell radiusN = reuse pattern (the cluster size, which is 7).

Thus, for a 7-cell group with cell radius R = 3 miles, the frequency reuse distance D is 13.74 miles.

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Personal Communication System (PCS)Personal Communication System (PCS)

Problems with cellular structure

How to locate of a mobile unit in the entire coverage area?

Solution: Location management

How to maintain continuous communication between two parties in the presence of mobility?

Solution: Handoff

How to maintain continuous communication between two parties in the presence of mobility?

Solution: Roaming

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HandoffA process, which allows users to remain in touch, even

while breaking the connection with one BS and

establishing connection with another BS.

Old BS New BS

MSC

Old BS New BS

MSC

MSC

Old BS New BS New BSOld BS

MSC

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Handoff

To keep the conversation going, the Handoff

procedure should be completed while the MS (the

bus) is in the overlap region.

G

Old BS New BS

Cell overlap region

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Handoff types with reference to the network

Intra-system handoff or Inter-BS handoff

The new and the old BSs are connected to

the same MSC.

Old BS New BS

MSC

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Steps

1. The MU (MS) momentarily suspends

conversation and initiates the handoff

procedure by signaling on an idle (currently

free) channel in the new BS. Then it resumes

the conversation on the old BS.

Old BS

MSC

New BS

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2. Upon receipt of the signal, the MSC transfers the encryption

information to the selected idle channel of the new BS and

sets up the new conversation path to the MS through that

channel. The switch bridges the new path with the old path

and informs the MS to transfer from the old channel to the

new channel.

Old BS

MSC

New BS

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3. After the MS has been transferred to the new BS, it signals

the network and resumes conversation using the new

channel.

Old BS

MSC

New BS

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4. Upon the receipt of the handoff completion signal, the

network removes the bridge from the path and releases

resources associated with the old channel.

Old BS

MSC

New BS

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Handoff types with reference to the network

Intersystem handoff or Inter-MSC handoff

The new and the old BSs are connected to

different MSCs.

BS1

MSC B

BS2

MSC A

BS1

MSC B

BS2

MSC A

PSTN

TrunkTrunk

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Roaming

Administrative constraints

Billing.

Subscription agreement.

Call transfer charges.

User profile and database sharing.

Any other policy constraints.

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Roaming

Technical constraints

Bandwidth mismatch. For example, European

900MHz band may not be available in other

parts of the world.

Service providers must be able to

communicate with each other. Needs some

standard.

Mobile station constraints.

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RoamingTwo basic operations in roaming management are

Registration (Location update): The process of

informing the presence or arrival of a MU to a

cell. Location tracking: the process of locating the

desired MU.

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Registration

Two-Tier Scheme

HLR: Home Location Register

A HLR stores user profile and the

geographical location.

VLR: Visitor Location Register

A VLR stores user profile and the current

location who is a visitor to a different cell that

its home cell.

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Registration

Two-Tier Scheme steps. MU1 moves to cell 2.

MU1

MU1

Cell 1 Cell 2

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Registration

Steps

1. MU1 moves to cell 2. The MSC of cell 2 launches a

registration query to its VLR 2.

2. VLR2 sends a registration message containing MU’s

identity (MIN), which can be translated to HLR address.

3. After registration, HLR sends an acknowledgment

back to VLR2.

4. HLR sends a deregistration message to VLR1 (of cell

1) to delete the record of MU1 (obsolete). VLR1

acknowledges the cancellation.

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Location tracking

Steps

1. VLR of cell 2 is searched for MU1’s profile.

2. If it is not found, then HLR is searched.

3. Once the location of MU1 is found, then the

information is sent to the base station of cell 1.

4. Cell 1 establishes the communication.

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Location trackingTwo-Tier Scheme steps location search

Source-mss

Destls

Sourcels

Id LSDest Dest-ls - -

Id HLSDest Dest-HLS - -

DestHLS

Id MSSDest Dest-mss - -

DestSrc

1

2

3

4

9

5

6

87

10

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Location trackingTwo-Tier Scheme steps location update

New-lsOld-ls

HLS

MU

1

23

10

9

5

6

47

8

Id HLSMU HLS - -

Id MSSMU New-mss - -

Id LSMU New-ls - -

New-mss

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Mobile Database Systems (MDS)Mobile Database Systems (MDS)

A Reference Architecture (Client-Server model)

MSC MSC

DB DB HLR VLR

BSC BSC

DBS DBS

MU BS

MU

MU

BS

MU

BS

MU

Fixed host

Fixed host

PSTN

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Data Processing Issues Processing at the Server Processing at the Client Update Propagation and Installation Consistency Management Less Serious:

– Concurrent Transactions– Client Data Recovery

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Database Issues in Mobile Computing

Query and Transaction Processing Replication Management Location ManagementLimitations

– Data Distribution, Mobility Management and Scalability– Role of wireless medium in info distribution– Dealing with short battery life– Dealing with prolonged disconnection

Periods– Bandwidth Management

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Mobility Management andScalability Location management Changing topologies Handoffs Resource finding Replication Resource sharing

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Bandwidth Management

Clients assumed to have weak and/or

unreliable communication capabilities Broadcast--scalable but high latency On-demand--less scalable and requires

more powerful client, but better response Client caching allows bandwidth

conservation

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Energy Management

Battery life expected to increase by only

20% in the next 10 years Reduce the number of messages sent Doze modes Power aware system software Power aware microprocessors Indexing wireless data to reduce tuning

time

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Large Impact Distributed data management Querying wireless data Handling/representing fast-changing data Scale Tariff-driven query optimization Security User interfaces

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Query Processing

New Issues– Energy Efficient Query Processing

– Location Dependent Query Processing

Old Issues - New Context– Cost Model

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Location Management New Issues

– Tracking Mobile Users Old Issues - New Context

– Managing Update Intensive Location Information

– Providing Replication to Reduce Latency for Location Queries

– Consistent Maintenance of Location Information

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Transaction Processing

New Issues– Recovery of Mobile Transactions– Lock Management in Mobile Transaction

Old Issues - New Context Extended Transaction Models

– Partitioning Objects while Maintaining Correctness

Dissemination-based Data Delivery Using Broadcast Disks

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Broadcast Disk Proposes a mechanism called Broadcast

Disks to provide database access to mobile clients.

Server continuously and repeatedly broadcasts data to a mobile client as it goes by.

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

Exploits the client storage resources for caching data.

Server Broadcast Programs Data server must construct a broadcast

“program” to meet the needs of the client population.

Server would take the union of required items and broadcast the resulting set cyclically.

Single additional layer in a client’s memory hierarchy - flat broadcast.

In a flat broadcast the expected wait for an item on the broadcast is the same for all items.

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Server Broadcast Programs


Broadcast Disks are an alternative to flat broadcasts.

Broadcast is structured as multiple disks of varying sizes, each spinning at different rates.

Server Broadcast ProgramsFlat Broadcast

Skewed Broadcast

Multi-disk Broadcast

Server Broadcast Programs For uniform access probabilities a

flat disk has the best expected performance.

For increasingly skewed access probabilities, non-flat disk programs perform better.

Multi-disk programs perform better than the skewed programs.

Server Broadcast ProgramsGenerating a multi-disk broadcast

Number of disks (num_disks) determine the number of different frequencies with which pages will be broadcast.

For each disk, the number of pages and the relative frequency of broadcast (rel_freq(i)) are specified.

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Client Cache Management Improving the broadcast for one

probability access distribution will hurt the performance of other clients with different access distributions.

Therefore the client machines need to cache pages obtained from the broadcast.

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Client Cache Management

With traditional caching clients cache the data most likely to be accessed in the future.

With Broadcast Disks, traditional caching may lead to poor performance if the server’s broadcast is poorly matched to the clients access distribution.

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Client Cache Management In the Broadcast Disk system, clients

cache the pages for which the local probability of access is higher than the frequency of broadcast.

This leads to the need for cost-based page replacement.

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Client Cache Management

One cost-based page replacement strategy replaces the page that has the lowest ratio between its probability of access (P) and its frequency of broadcast (X) - PIX

PIX requires the following:

1 Perfect knowledge of access probabilities.

2 Comparison of PIX values for all cache resident pages at cache replacement time.

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Client Cache Management Another page replacement strategy adds

the frequency of broadcast to an LRU style policy. This policy is known as LIX.

LIX maintains a separate list of cache-resident pages for each logical disk

Each list is ordered based on an approximation of the access probability (L) for each page.

A LIX value is computed by dividing L by X, the frequency of broadcast. The page with the lowest LIX value is replaced.

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Prefetching

An alternative approach to obtaining pages from the broadcast.

Goal is to improve the response time of clients that access data from the broadcast.

Methods of Prefetching:

Tag Team Caching

Prefetching Heuristic

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Prefetching Tag Team Caching - Pages continually

replace each other in the cache. For example two pages x and y, being

broadcast, the client caches x as it arrives on the broadcast. Client drops x and caches y when y arrives on the broadcast.

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Prefetching Simple Prefetching Heuristic Performs a calculation for each page that

arrives on the broadcast based on the probability of access for the page (P) and the amount of time that will elapse before the page will come around again (T).

If the PT value of the page being broadcast is higher than the page in cache with the lowest PT value, then the page in cache is replaced.

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Read/Write Case With dynamic broadcast there are three

different changes that have to be handled.

1 Changes to the value of the objects being broadcast.

2 Reorganization of the broadcast.3 Changes to the contents of the

broadcast.

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Conclusion

Broadcast Disks project investigates the use of data broadcast and client storage resources to provide improved performance, scalability and availability in networked applications with asymmetric capabilities.

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Mobility System conguration is no longer static:

the center of activity, the topology, the system load, and

locality, change dynamically need to search to locate objects various forms of heterogeneity

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Wireless Communications

offer less bandwidth more expensive less reliable Consequently, connectivity is weak and

often intermittent

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Portable Devices

light and small to be easily carried around Such considerations, in conjunction with a given

cost and level of technology ) mobile elements with less

resources (e.g., memory, screen size and disk capacity) reliance on battery can be more easily accidentally damaged,

stolen, or lost, thus, less secure and reliable

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Mobile units are still characterized as: unreliable and prone to hard failures,

i.e., theft, loss or accidental damage, resource-poor relative to static hosts. Examples: InfoPad [16] and ParcTab

[28] projects

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Adaptability A mobile system is presented with resources

of varying number and quality: Connectivity conditions vary from total

disconnections to full connectivity Available resources are not static either, for

instance a docked" mobile computer may have access

to a larger display or memory. the location of mobile elements changes and

so does the network conguration and the center of computational activity

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Example: during disconnection, a mobile host may work

autonomously, while during periods of strong connectivity, depend

heavily on the xed network sparing its scarce local resources

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Disconnections: disconnected operation - autonomous

operation of a mobile host during disconnection. Weak connectivity: Operation should be tuned

for communication environments characterized by low bandwidth, high latency, and expensive prices.

Mobility: Basic support such as as establishing new communication links as well as advanced support such as migrating executing processes and database transactions in progress.

Failure recovery: Since mobile elements are prone to hard failures, methods for failure handling and recovery are important.

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Data Dissemination by Broadcast

Pull-based data delivery or on demand data delivery: A client

explicitly requests data items from the server. Push-based data delivery: The server

repetitively broadcasts data to a client population without a specic

request. Clients monitor the broadcast and retrieve the data

items they need as they arrive.

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Applications: Dissemination-based: information feeds

such as stock quotes and sport tickets, electronic newsletters, mailing lists, traffic and weather information systems, cable TV on the Internet

Commercial Products for example: the AirMedia's Live Internet broadcast

network [6] Hughes Network Systems' DirectPC [26]

Teletext and Videotex systems [11, 28]

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The Datacycle project [16] at Bellcore: a database circulates on a high bandwidth network (140 Mbps). Users query the database by ltering information via special massively parallel transceivers.

The Boston Community Information System (BCIS) [18]:

broadcast news and information over an FM channel to clients

with personal computers equipped with radio receivers

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Hybrid Delivery Push vs Pull Push suitable when information is transmitted

to a large number of clients with overlapping interests the server saves several messages

the server is prevented from being overwhelmed by client requests.

Push is scalable: performance does not depend on the number of clients Pull cannot scale beyond the capacity of the server

or the network. In push, access is only sequential; Thus,

access latency degrades with the volume of data In pull, clients play a more active role

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Hybrid Delivery clients are provided with an uplink

channel, called backchannel, to send messages to the server. Sharing the channel : if the same channel is used for both

broadcast delivery and for the transmission of the replies to on

demand requests

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Use of the backchannel - to provide feedback and prole information to

the server - to directly request data Which pages? to avoid overwhelming the

server Page i not in cache and the number of items

scheduled to appear before i on the broadcast is greater than a

threshold parameter

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Selective Broadcast Broadcast an appropriately selected subset of items and provide the rest on demand In [25], the broadcast is used as an air-cache for storing frequently requested data. The broadcast content continuously adjusts to match the hot-spot of the database. The hot-spot is calculated by observing broadcast misses indicated by explicit requests for data not on the broadcast. In [19]: the database is partitioned into: a \publication group" that is broadcast and an \on demand" group. The criterion for partitioning is to minimize the backchannel requests while constraining the response time below a predened upper limit.

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On Demand Broadcast the server chooses the next item to broadcast

on every broadcast tick based on the requests for data it has

received Various strategies [28]: broadcast the pages

in the order they are requested (FCFS), or the page with the

maximum number of pending requests. A parameterized algorithm for large-scale

data broadcast based only on the current queue of pending

requests [7

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Organization of Broadcast Data

Access time: average time elapsed from the moment a client

expresses its interest to an item to the receipt of the item on the

broadcast channel Tuning time: the amount of time spent

listening to the broadcast channel Organize the broadcast to minimize

access and tuning time

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Efficient Concurrency Control for Broadcast EnvironmentsJayavel Shanmugasundaram

Arvind Nithrakashyap

Rajendran Sivasankaran

Krithi Ramamritham

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Outline Broadcast environments Inapplicability of existing techniques Suitable correctness criterion Mechanisms Performance Results Conclusion

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Why Broadcast Data? Millions of clients that need to see current

and consistent data Server handling all client requests

==> not scalable More scalable solution:

Periodically broadcast all data items Clients read items off broadcast Datacycle [Herman], Broadcast Disks

[Acharya]

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Example: eAuctions

Numerous potential clients

Only a small fraction contact server to offer bids

Need access to current and consistent data

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Broadcast Environment Characteristics

Large number of clients Mobile clients with scarce power resource

==> Low client to server bandwidth

Plentiful server to client bandwidth==> Asymmetric communication medium

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Mutually Consistent Reads

R(x) R(y) R(z)

time (broadcast cycles)

Are x, y, and z mutually consistent?

TrBegin TrEnd

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Why Not Traditional CC Techniques?

Approach 1: Dynamic conflict resolution – Excessive communication– e.g., locking:

• acquiring read locks by client transactions• server swamped with lock requests• client uses precious uplink bandwidth

Approach 2: Avoid potential serializability conflicts

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Schedules

C4W4(y)Server W2(x) C2

ClientA R1(y)R1(x)

time

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Serialization Orders


ClientA R1(y)R1(x)

T2 T4

T4T1T2

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Serialization Orders


ClientA R1(y)R1(x)

R3(x) R3(y)ClientB

T2 T4

T4T1T2

Even if ClientB does not exist, ClientA will have to abort transaction T1

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Serializability?

Serializability - a global property

All read only transactions:– Required to see same serial order of update

transactions, even if executing at different clients– Required to be serializable w.r.t. all update transactions,

even if updates do not affect values read

Inappropriate for broadcast environments

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Broadcast Data Requirements

Mutual consistency – server maintains mutually consistent data

– clients read mutually consistent data

Currency – clients see data that is current

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A Sufficient Criterion

• All update transactions are serializable.

• Each read-only transaction is serializable with respect to the update transactions it (directly or indirectly) reads from.


ClientA R1(y)R1(x)

R3(x) R3(y)ClientB

T2T4

T4T1

T2T3

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A Sufficient Criterion

• All update transactions are serializable.

• Each read-only transaction is serializable with respect to the update transactions it (directly or indirectly) reads from.

external consistency [Weihl 87] update consistency [Bober and Carey 92]

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Implications

Decoupled correctness criterion– Clients need not communicate with server

or other clients

Weaker correctness criterion– Reduces unnecessary aborts

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The Algorithm F-Matrix

Server functionality Client functionality Nature of Control Information

– broadcast by the server with the data– helps clients determine consistency of

reads

Client read-only validation protocol

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Server Functionality

Ensures conflict serializability of update transactions

Broadcasts during each cycle– Committed values of data items at start of

cycle– Control matrix

Incrementally maintains control matrix as updates occur

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Client Functionalityconsult control information transmitted during that cycle to determine whether the read operation can proceed

if read operation cannot proceed the transaction is aborted.

Read

update tr : (write set + values) along with (read set + cycle numbers) sent to server

read tr : commit succeeds

Commit

Writeperformed on a local copy of the data item in the client.

no checks are made

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Control Matrix: Intuition


Client R1(y)R1(x)

C4R4(x) W4(y)Server W2(x) C2

Client R1(y)R1(x)

T is currently reading yT had read xDid any tr that affected y change x after T read it?

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Control MatrixObjects: n objects all initialized at cycle 0

C: n x n control matrix

C(x,y) = max( cycle in which T commits ), where

T affects the latest committed value of y and also writes to x

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Precond. for Consistent ReadsT previously read x from broadcast cycle b

RT = set of (x ,b) pairs

C is the matrix at the beginning of current cycle

read y iff read-condition(y) holds:

forall (x,b) in RT, C(x,y) < b

i.e., no transaction that affected y wrote x after t read x

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Smaller Control Matrix

Partition objects into groups Control matrix: n x numgroups SC(x,s) = max y in s C(x, y)

Updating an object in s = update to any object in s

Fewer entries to transmit compared to Cgroup 1 group2

read-condition(y): forall (x , b) in RT SC(i , s) < b

T is currently reading yT had read xNo tr that affected any object in y ‘s group

changed x after T read it

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Group Size Increasing size of group =>

more unnecessary conflicts

Reducing size of group => increased control information overhead.

– n groups => F-Matrix– one group => Datacycle

• achieves serializability

Read-condition for Datacycle :

no previously read object has been updated

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R-Matrix

To achieve Mutual Consistency

Read condition:

objects previously read have not been updated by other transactions or

the object being read has not been updatedsince the beginning of the transaction

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Effect of Client Tr. Length

F-Matrix -- has best perf.-- scales very well

DatacycleR-MatrixF-MatrixF-Matrix-ideal

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Summary of Results

F-Matrix > R-Matrix > Datacycle

– Weaker abort condition leads to better response times

F-Matrix is highly scalable with respect to

– Client/Server transaction length

– Server transaction rate

– Number of Objects/Size of Objects

R-Matrix better only at very small object sizes

In many cases F-Matrix is very close to F-Matrix-ideal

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Conclusion

Need for mutual consistency + currency Efficient mechanism - F-matrix R-matrix is a low overhead alternative F-matrix delivers!

In Paper: Caching to exploit weak currency requirements

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Examples

Business data, e.g., Vitria, Tibco Election coverage data Stock related data Traffic information Electronic auctions

Data Server

1 mobile data management sanjay kumar madria department of computer science university of...

Documents

wireless network access

different network performance

different networks

wireless connectivity

contracting slide

consistent slide

different applications

local data