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Mobile Data Management
Sanjay Kumar Madria
Department of Computer Science
University of Missouri-Rolla
Rolla, MO 65401
1
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

2
Mobility Constraints
CPU
 Power
 Bandwidth
 Delay tolerance
 Physical size
 Constraints on peripherals and
GUIs (modality of interaction)
 Locations change dynamically

3
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

4
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
5
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 information
Limitations
 Short connect time per session
 Infrequent connections
 Clients may remain dormant for extended periods
of time
 Clients not reachable from servers at all times
6
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
7
Device mobility

Plug in laptop at home/work on Ethernet
– Occasional long breaks in network access
– Wired network access only (connected => wellconnected)
– 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
8
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
9
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
10
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
11
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
12
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
13
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
14

Most RDBMS vendors support the ISDB
scenario - but no design and optimization
aids
 Specialized Environments for ISDB apps:
Sybase Remote Server
Synchrologic iMOBILE
Microsoft SQL server - mobile app support
Oracle Lite
Xtnd-Connect-Server (Extended
Technologies)
Scoutware (Riverbed Technologies)
15
Personal Communication System
(PCS)
Wireless Components
MSC (MTSO)
BS
MS
MS
Wireless
component
Cell
16
Personal Communication System
(PCS)
Mobile cells
Metropolitan area
Metropolitan area
BS
Base Station
Coverage area in one cell
BS
BS
Coverage area in three cells
Large cells.
Low density
Small cells.
High density
Smaller cells.
Higher density
17
Personal Communication System
(PCS)
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.
MSC
PSTN
18
Personal Communication System
(PCS)
Frequency reuse
A
2
7
3
7
4
5
2
3
1
1
6
A
2
6
4
5
D7
3
1
6
A
A
A
4
5
A
A
D
 3N
R
D = distance between cells using the same frequency
R = cell radius
N = 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.
19
Personal Communication System (PCS)
Problems with cellular structure

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

How to locate of a mobile unit in the entire
coverage area?
Solution: Location management
20
Personal Communication System
(PCS)
Handoff
A process, which allows users to remain in touch, even
while breaking the connection with one BS and
establishing connection with another BS.
MSC
MSC
New BS Old BS
Old BS
MSC
MSC
Old BS
New BS
New BS
Old BS
New BS
21
Personal Communication System
(PCS)
Handoff
To keep the conversation going, the Handoff
procedure should be completed while the MS (the
bus) is in the overlap region.
Cell overlap region
G
Old BS
New BS
22
Personal Communication System
(PCS)
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.
MSC
Old BS
New BS
23
Personal Communication System
(PCS)
Intra-system handoff or Inter-BS handoff
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.
MSC
Old BS
New BS
24
Personal Communication System
(PCS)
Intra-system handoff or Inter-BS handoff
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.
MSC
Old BS
New BS
25
Personal Communication System
(PCS)
Intra-system handoff or Inter-BS handoff
3.
After the MS has been transferred to the new BS, it signals
the network and resumes conversation using the new
channel.
MSC
Old BS
New BS
26
Personal Communication System
(PCS)
Intra-system handoff or Inter-BS handoff
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.
MSC
Old BS
New BS
27
Personal Communication System
(PCS)
Handoff types with reference to the network

Intersystem handoff or Inter-MSC handoff
The new and the old BSs are connected to
different MSCs.
PSTN
BS1
MSC A
Trunk
BS2
MSC B
BS1
MSC A
Trunk
BS2
MSC B
28
Personal Communication System
(PCS)
Roaming
Administrative constraints

Billing.

Subscription agreement.

Call transfer charges.

User profile and database sharing.

Any other policy constraints.
29
Personal Communication System
(PCS)
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.
30
Personal Communication System
(PCS)
Roaming
Two 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.
31
Personal Communication System
(PCS)
Registration
Two-Tier Scheme
HLR: Home Location Register
A HLR stores user
geographical location.
profile
and
the
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.
32
Personal Communication System
(PCS)
Registration
Two-Tier Scheme steps. MU1 moves to cell 2.
Cell 1
Cell 2
MU1
MU1
33
Personal Communication System
(PCS)
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.
34
Personal Communication System
(PCS)
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.
35
Personal Communication System
(PCS)
Location tracking
Two-Tier Scheme steps location search
5
Dest
HLS
Id
HLS
Dest Dest-HLS
3
Id
LS
Dest Dest-ls
-
6
4
Source
ls
9
Id
MSS
Dest Dest-mss
-
8
Dest
ls
7
10
2
Source-mss
1
36
Src
Dest
Personal Communication System
(PCS)
Location tracking
Two-Tier Scheme steps location update
5
Id
LS
MU New-ls
Id
MSS
MU New-mss
-
HLS
6
10
4
9
New-ls
Old-ls
3
Id HLS
MU HLS
-
2
7
8
New-mss
1
37
MU
Mobile Database Systems (MDS)
A Reference Architecture (Client-Server model)
PSTN
DB
DB
DBS DBS
HLR
VLR
MSC
MSC
BSC
BSC
Fixed host
Fixed host
BS
MU
MU
MU
BS
BS
MU
MU
38
Data Processing Issues

Processing at the Server
 Processing at the Client
 Update Propagation and Installation
 Consistency Management
 Less Serious:
– Concurrent Transactions
– Client Data Recovery
39
40
Database Issues in Mobile
Computing

Query and Transaction Processing
 Replication Management
 Location Management
Limitations
–
–
–
–
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
41
Mobility Management and
Scalability

Location management
 Changing topologies
 Handoffs
 Resource finding
 Replication
 Resource sharing
42
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
43
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
44
Large Impact

Distributed data management
 Querying wireless data
 Handling/representing fast-changing data
 Scale
 Tariff-driven query optimization
 Security
 User interfaces
45
Query Processing

New Issues
– Energy Efficient Query Processing
– Location Dependent Query
Processing
 Old Issues - New Context
– Cost Model
46
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
47
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
48
Dissemination-based Data Delivery
Using Broadcast Disks
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.
50
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.
Server Broadcast Programs
52
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 Programs
Flat 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 Programs
Generating 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.
Server Broadcast Programs
57
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.
58
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.
59
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.
60
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.
61
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 cacheresident 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.
62
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

63
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.

64
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.
65
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.
66
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.
67
68
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

69
Wireless Communications
offer less bandwidth
 more expensive
 less reliable
 Consequently, connectivity is weak and
often intermittent

70
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
71
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

72
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
73
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

74





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.
75
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.
76
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]

77

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
78
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
79
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

80






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
81
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.
82
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
83
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

84
Efficient
Concurrency
Control for
Broadcast
Jayavel Shanmugasundaram
Environments
Arvind Nithrakashyap
Rajendran Sivasankaran
85
Outline
Broadcast environments
 Inapplicability of existing techniques
 Suitable correctness criterion
 Mechanisms
 Performance Results
 Conclusion

86
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]
87
Example: eAuctions

Numerous potential clients

Only a small fraction contact
server to offer bids

Need access to current and
consistent data
88
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
89
Mutually Consistent Reads
TrBegin
R(x)
R(y)
R(z)
TrEnd
time (broadcast cycles)
Are x, y, and z mutually consistent?
90
Outline
Broadcast environments
 Inapplicability of existing techniques
 Suitable correctness criterion
 Mechanisms
 Performance Results
 Conclusion

91
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
92
Schedules
Server
ClientA
W2(x) C2
W4(y) C4
R1(x)
R1(y)
time
93
Serialization Orders
Server
W2(x) C2
W4(y) C4
T2 T4
ClientA
R1(x)
R1(y)
T4T1T2
94
Serialization Orders
Server
W2(x) C2
W4(y) C4
T2 T4
ClientA
R1(x)
R1(y)
T4T1T2
ClientB
R3(x)
R3(y)
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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Outline
Broadcast environments
 Inapplicability of existing techniques
 Suitable correctness criterion
 Mechanisms
 Performance Results
 Conclusion

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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.
Server
W2(x) C2
W4(y) C4
T2T4
ClientA
R1(y)
R1(x)
T4T1
ClientB
T2T3
R3(x) R3(y)
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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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Outline
Broadcast environments
 Inapplicability of existing techniques
 Suitable correctness criterion
 Mechanisms
 Performance Results
 Conclusion

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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 Functionality
Read
consult control information transmitted during that cycle
to determine whether the read operation can proceed
if read operation cannot proceed the transaction is aborted.
Write
Commit
performed on a local copy of the data item in the client.
no checks are made
update tr : (write set + values) along with
(read set + cycle numbers) sent to server
read tr : commit succeeds
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Control Matrix: Intuition
W2(x) C2
Server
Client
R1(x)
Server
Client
W4(y) C4
R1(y)
W2(x) C2
R1(x)
T is currently reading y
T had read x
Did any tr that affected y
R4(x) W4(y) C4
R1(y)
change x after T read it?
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Control Matrix
Objects: 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 Reads
T 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 C
group 1
read-condition(y):
forall (x , b) in RT
group2
SC(i , s) < b
T is currently reading y
T had read x
No 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 updated
since the beginning of the transaction
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Outline
Broadcast environments
 Inapplicability of existing techniques
 Suitable correctness criterion
 Mechanisms
 Performance Results
 Conclusion

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Effect of Client Tr. Length
F-Matrix
-- has best perf.
-- scales very well
Datacycle
R-Matrix
F-Matrix
F-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
Data Server
Business data, e.g., Vitria, Tibco
 Election coverage data
 Stock related data
 Traffic information
 Electronic auctions

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