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Mobile Databases

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Mobile ad hoc network (MANET) is an autonomous system ... Mobile transaction may execute on ... a mobile client may often experience voluntary/involuntary ... – PowerPoint PPT presentation

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Title: Mobile Databases


1
Mobile Databases
  • Ph. D. student Weigang Ni
  • Data Management in Adaptive Broadcast
    Environments

2
Mobile databases
  • Mobile database assumes a traditional database
    requiring ACID properties
  • Clients are mobile (wireless)
  • Environment requires new strategies for
  • Concurrency control
  • Processing transactions
  • Data dissemination
  • Querying location dependency

3
Challenges in data management
  • Unique features in mobile computing environments
  • Asymmetric communication bandwidth
  • Server has much higher communication capacity
    than the clients
  • Frequent disconnections
  • Clients cannot keep network connection all the
    time
  • Power limitations
  • Mobile clients run on battery power
  • Energy saving is a critical issue

4
Network Architecture
5
MANET
  • While typically assume DB server on fixed network
  • Mobile ad hoc network (MANET) is an autonomous
    system consisting of
  • mobile nodes interconnected by wireless links
  • often operating in stand-alone fashion
  • without any assistance from a pre-existing
    network infrastructure
  • DB server can be part of MANET
  • Very complex most research in routing issues

6
Caching
  • To compensate for high latency and unreliable
    connectivity
  • Frequently accessed data is cached
  • Can work offline if necessary
  • Dozing energy conserving state, unreachable
    state
  • Can add proxies for unreachable components
  • Proxies keep track of updates to cache

7
Database Management Issues
  • Entire DB distributed among wired components
  • Full or partial replication
  • Base station or fixed host has DBMS functionality
  • Also able to locate mobile units
  • Query and transaction management features for
    mobile environment
  • Still require ACID properties

8
DM Issues
  • Alternatively DB distributed among wired and
    wireless components
  • Data management shared among base stations, fixed
    hosts and mobile units

9
Data distribution and replication
  • Data unevenly distributed among BS and MU
  • Consistency constraints and cache management
  • Cache provides most frequently accessed data

10
Transaction models
  • Mobile transaction may execute on several BSs
  • Central coordination lacking if data distributed
    among wireless components
  • Long lived transactions
  • ACID properties difficult to guarantee

11
Query processing
  • Must know location of data
  • Query optimization more difficult because of
    mobility and resource changes of MU
  • MU may be in transit or may cross cell boundaries

12
Recovery and Fault tolerance
  • Site, media, transaction and communication
    failures
  • Voluntary shutdown not a site failure
  • Transaction failures can occur during handoff

13
Location-based services
  • Location dependent cache information may become
    stale
  • Frequently updated location dependent queries
  • Then applying spatial queries to refresh cache -
    problem

14
Security
  • Mobile data less secure than data at fixed
    location
  • Data is more volatile
  • Must manage and authorize access to critical data

15
Data dissemination
  • How does a mobile client access the database
  • Widely accepted approach is to broadcast the data
    Data on Air

16
Related Research -- Data Dissemination (cont.)
  • Broadcast-based data dissemination approaches
  • Push-based data broadcasting
  • Pull-based data broadcasting

17
Push-based broadcasting
  • Data contents within a file or database are
    repeatedly broadcast through the broadcast
    channel
  • channel becomes a disk
  • clients can retrieve data as it goes by
  • expected wait time for a data item is the same

18
Flat broadcasting
19
Broadcast Disks
  • broadcast data in different frequencies according
    to their relevant importance
  • multi-level memory hierarchy
  • hot data are broadcast more frequently then cold
    data
  • Data with similar access frequency are grouped
    into disks

20
Server Broadcast Program
21
Pull-based broadcast
  • also called adaptive approaches
  • data items are broadcast on-demand
  • only requested data will appear as data on air
  • How do decide which page to broadcast next?

22
Pull-based
  • Data broadcasting is prioritized according to
    some metrics
  • Most common algorithms are
  • First come Fist Served (FCFS) broadcasts the
    pages in the order they are requested.
  • Most Requests First (MRF) broadcasts the page
    with maximum number of pending requests.
  • Longest Wait First (LWF) selects the page that
    has the largest total waiting time, i.e., the sum
    of the time that all pending request for the item
    have been waiting. (RW is approximation)

23
Related Research -- Indexing
  • Clients can save battery power by turning into
    active mode only when interested data are
    broadcast
  • (1, m) index method (Imielinski, et al. 94)
  • Index is broadcast m times during the broadcast
    of one version of the file

24
Related Research Indexing (cont.)
25
Data Consistency
  • Challenges in mobile environments
  • Database server typically is stateless,
    especially under broadcast systems
  • Mobile clients often cannot maintain a sustained
    network connection
  • How to ensure conflict serializability?

26
Data Consistency
  • a mobile client may often experience
    voluntary/involuntary disconnections
  • Then, it can only read and update data copied
    onto their local cache
  • What if data cached updated during disconnection?
  • Virtual Locks (Ni 2003)

27
Lazy Data Request (LDR)
  • Pull-based data broadcasting data are broadcast
    on demand (Stathatos, et al. 96)
  • Scheduling algorithms
  • First Come First Serve (FCFS)
  • Most Requests First (MRF)
  • Longest Wait First (LWF)
  • Requests times Wait (R W)
  • Other algorithms based broadcast histories,
    estimation of the probabilities of access for
    each data item.

28
Lazy Data Request (cont.)
  • Existing algorithms mainly concern data access
    time.
  • Whenever a client has a data request, it sends
    the request to the server Eager Data Request
    (EDR).
  • Sending message consumes more battery power than
    receiving message.

29
Lazy Data Request (cont.)
  • Motivation wanted data may have already been
    requested by other clients. Why not wait instead.
    Two possible results.
  • Issues need to be addressed
  • Mobile clients do not communicate with each
    other. Therefore, they cannot decide whether to
    wait or go ahead and send the request
  • The system load changes dynamically. A predefined
    waiting time will not work well.

30
Lazy Data Request (cont.)
  • Features of Lazy Data Request
  • Client do not need to contact the server to get
    the system load information and waiting time.
  • The waiting time is dynamically changing
    according to system load.
  • LDR approach can apply to nearly all the existing
    on-demand broadcast algorithms

31
Server-side algorithm of LDR
  • Step1. Let n be the total number of requested
    data items
  • Step 2. Choose ?n data items to be broadcast
    next based on some scheduling algorithm (0 lt ?
    1)
  • Step 3. Clear all existing requests for these ?n
    data times.
  • Step 4. Broadcast the index section consisting of
    these ?n data items.
  • Step 5. Broadcast the data items.
  • Eg. Will broadcast (? 100) of data items

32
Client-side algorithm of LDR
  • Wait until wanted data or index section is
    broadcast
  • If wanted data items come
  • download the data
  • drop the local pending request
  • else
  • check the index section
  • if wanted data ids in index section
  • wait until data are broadcast
  • else
  • send the pending request(s) to the server

33
Discussion
  • Algorithm still work without using index.
    However, index makes the data broadcast more
    predictable and further saves the data request
    messages.
  • Adjust the value of ?,
  • If ? 1, LDR becomes first come first served
    (FCFS) algorithm
  • If ? is very small, LDR virtually becomes EDR as
    every time only a couple of data items are chosen
  • Client waiting time is bounded.

34
Experimental results
35
Experimental results (cont.)
36
Conclusions
  • Data management in mobile environments
  • Concurrency control
  • Algorithms proposed produce serializable
    histories
  • Outperform existing algorithms
  • Adaptive data broadcasting
  • Algorithm proposed shows number of data request
    messages can be reduced
  • Data access time does not increase
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