Mobile Data Management

1
Mobile Data Management

• Sanjay Kumar Madria
• Department of Computer Science
• University of Missouri-Rolla
• Rolla, MO 65401

2
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

3
Mobility Constraints

• CPU
• Power
• Bandwidth
• Delay tolerance
• Physical size
• Constraints on peripherals and GUIs (modality of
  interaction)
• Locations change dynamically

4
Why Mobile Data Mgmt?

• Wireless Connectivity and use of PDAs ,
• 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

5
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

6
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

7
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

8
Device mobility

• Plug in laptop at home/work on Ethernet
• Occasional long breaks in network access
• Wired network access only (connected gt
  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

9
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

10
People mobility

• Phone available at home or at work
• Multiple phone numbers to reach me
• Breaks in my reachability when Im 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 senders
  device or message content

11
Mobility means changes

• How does it affect the following?
• Hardware
• Lighter
• More robust
• Lower power
• Wireless communication
• Cant tune for stationary access
• Network protocols
• Name changes
• Delay changes
• Error rate changes

12
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

13
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

14
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
  BW
• Available resources
• Files, printers, displays, power, even routing

15

• 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)

16
Personal Communication System (PCS)

• Wireless Components

17
Personal Communication System (PCS)

• Mobile cells

18
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.

19
Personal Communication System (PCS)

• Frequency reuse

20
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

21
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.
22
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.

23
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.

24
Personal Communication System (PCS)

• Intra-system handoff or Inter-BS handoff

• Steps
• 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.

25
Personal Communication System (PCS)

• Intra-system handoff or Inter-BS handoff

1. 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.

26
Personal Communication System (PCS)

• Intra-system handoff or Inter-BS handoff

1. After the MS has been transferred to the new BS,
   it signals the network and resumes conversation
   using the new channel.

27
Personal Communication System (PCS)

• Intra-system handoff or Inter-BS handoff

1. Upon the receipt of the handoff completion
   signal, the network removes the bridge from the
   path and releases resources associated with the
   old channel.

28
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.

29
Personal Communication System (PCS)

• Roaming

Administrative constraints

• Billing.
• Subscription agreement.
• Call transfer charges.
• User profile and database sharing.
• Any other policy constraints.

30
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.

31
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.

32
Personal Communication System (PCS)

• 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.
33
Personal Communication System (PCS)

• Registration

Two-Tier Scheme steps. MU1 moves to cell 2.
34
Personal Communication System (PCS)

• Registration

• Steps
• MU1 moves to cell 2. The MSC of cell 2 launches
  a registration query to its VLR 2.
• VLR2 sends a registration message containing MUs
  identity (MIN), which can be translated to HLR
  address.
• After registration, HLR sends an acknowledgment
  back to VLR2.
• HLR sends a deregistration message to VLR1 (of
  cell 1) to delete the record of MU1 (obsolete).
  VLR1 acknowledges the cancellation.

35
Personal Communication System (PCS)

• Location tracking

• Steps
• VLR of cell 2 is searched for MU1s profile.
• If it is not found, then HLR is searched.
• Once the location of MU1 is found, then the
  information is sent to the base station of cell
  1.
• Cell 1 establishes the communication.

36
Personal Communication System (PCS)

• Location tracking

Two-Tier Scheme steps location search
37
Personal Communication System (PCS)

• Location tracking

Two-Tier Scheme steps location update
38
Mobile Database Systems (MDS)

• A Reference Architecture (Client-Server model)

39
Data Processing Issues

• Processing at the Server
• Processing at the Client
• Update Propagation and Installation
• Consistency Management
• Less Serious
• Concurrent Transactions
• Client Data Recovery

40
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41
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

42
Mobility Management andScalability

• Location management
• Changing topologies
• Handoffs
• Resource finding
• Replication
• Resource sharing

43
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

44
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

45
Large Impact

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

46
Query Processing

• New Issues
• Energy Efficient Query Processing
• Location Dependent Query Processing
• Old Issues - New Context
• Cost Model

47
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

48
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

49
Dissemination-based Data Delivery Using Broadcast
Disks

50
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.

51
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 clients memory
  hierarchy - flat broadcast.
• In a flat broadcast the expected wait for an item
  on the broadcast is the same for all items.

52
Server Broadcast Programs
53
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.

54
Server Broadcast Programs
Flat Broadcast
Skewed Broadcast
Multi-disk Broadcast
55
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.

56
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.

57
Server Broadcast Programs
58
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.

59
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 servers
  broadcast is poorly matched to the clients access
  distribution.

60
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.

61
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.

62
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.

63
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

64
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.

65
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.

66
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.

67
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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69

• 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

70
Wireless Communications

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

71
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

72

• 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

73

• 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

74

• 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

75

• 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.

76
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.

77

• 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

78

• 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

79
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

80

• 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

81

• 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

82
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.

83
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

84
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

85
Efficient Concurrency Control for Broadcast
Environments

• Jayavel Shanmugasundaram
• Arvind Nithrakashyap
• Rajendran Sivasankaran
• Krithi Ramamritham

86
Outline

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

87
Why Broadcast Data?

• Millions of clients that need to see current and
  consistent data
• Server handling all client requests
• gt not scalable
• More scalable solution
• Periodically broadcast all data items
• Clients read items off broadcast
• Datacycle Herman, Broadcast Disks Acharya

88
Example eAuctions

• Numerous potential clients
• Only a small fraction contact
• server to offer bids
• Need access to current and consistent data

89
Broadcast Environment Characteristics

• Large number of clients
• Mobile clients with scarce power resource
• gt Low client to server bandwidth
• Plentiful server to client bandwidth
• gt Asymmetric communication medium

90
Mutually Consistent Reads
R(x)
R(y)
R(z)
TrBegin
TrEnd
time (broadcast cycles)
Are x, y, and z mutually consistent?
91
Outline

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

92
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

93
Schedules
time
94
Serialization Orders
T2 T4
T4T1T2
95
Serialization Orders
T2 T4
T4T1T2
Even if ClientB does not exist, ClientA will have
to abort transaction T1
96
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

97
Outline

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

98
Broadcast Data Requirements

• Mutual consistency
• server maintains mutually consistent data
• clients read mutually consistent data
• Currency
• clients see data that is current

99
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.

C4
W4(y)
Server
W2(x)
C2
T2T4
ClientA
R1(y)
R1(x)
T4T1
ClientB
R3(x)
R3(y)
T2T3
100
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
101
Implications

• Decoupled correctness criterion
• Clients need not communicate with server or other
  clients
• Weaker correctness criterion
• Reduces unnecessary aborts

102
Outline

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

103
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

104
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

105
Client Functionality
106
Control Matrix Intuition
107
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
108
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) lt b i.e.,
no transaction that affected y wrote x
after t read x
109
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

group2
group 1
read-condition(y) forall (x , b) in RT
SC(i , s) lt 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
110
Group Size

• Increasing size of group gt more
  unnecessary conflicts
• Reducing size of group gt increased
  control information overhead.
• n groups gt F-Matrix
• one group gt Datacycle
• achieves serializability
• Read-condition for Datacycle

111
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
112
Outline

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

113
Effect of Client Tr. Length
F-Matrix -- has best perf. -- scales very well
114
Summary of Results

• F-Matrix gt R-Matrix gt 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

115
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

116
Related Work

• Broadcast Environments
• Datacycle Herman
• supports serializability
• Broadcast Disks Acharya
• consistency not considered
• ProMotion Chrysanthis
• assumes caches

117
Examples

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