Mobile Computing and Databases (modified from ICDE98)

1
Mobile Computing and Databases (modified from
ICDE98)

• Margaret H. Dunham
• Southern Methodist University
• Dept of Computer Science and Engineering
• Dallas, Texas 75275
• mhd_at_seas.smu.edu
• http//www.seas.smu.edu/mhd

2
Outline

• Introduction Data Management Issues
• Query Processing
• Data Broadcasting
• Transaction Processing
• Projects Products
• Conclusion

3
Mobile Computing Architecture
4
Terminology

• Fixed Network (FN)
• Base Station (BS) (Mobile Support Station -
  (MSS))
• Fixed Hosts (FH)
• Cell - Area covered by BS (1-2 miles)
• Handoff - Changing BS by intercell move
• Mobile Host (MH) (Mobile Unit (MU))

5
Wireless Networks

• Cellular
• High Cost
• Scalability Issue
• Limited Bandwidth 10 Kbps
• Wireless LAN
• Traditional LANs with wireless interface
• Low Cost
• Limited range 10-100 meters
• Bandwidth 10Mbps
• NCR Wavelan, Motorola ALTAIR

6
Wireless Networks (contd)

• Satellite Services
• Wide Coverage
• Very Expensive
• Low Bandwidth 1-2Mbps
• Paging Networks
• Wide Coverage
• Sky Tel, Motorola
• Slow (Ethernet 10Mbps FDDI or switched
  Ethernet 100Mbps ATM 155Mbps)
• Ad Hoc Networks

7
Handoff

• Changing BS due to movement between cells
• State information transferred
• Current handoffs in cellular phones may take up
  to a few seconds with breaks in conversation of
  100-300 ms.
• Soft - Temporarily connected to two BSs
• Hard - Only connected to one BS

8
Location Management

• Tracking mobile user
• User associated with home location server (Home
  Agent)
• May augment by searching in local area first
• May augment with user profiles
• Mobile IP 11,14
• Triangle Routing
• Route Optimization
• Location Control (Routing Agent)

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9
Location Management (contd)

• Active Badge (Cambridge,2)
• Track employees and route telephone calls
• Unique code emitted every 15 seconds
• Sensors placed in offices and corridors
• Location Information Replications
• No HLR
• Hierarchy of Location Servers
• Each server maintains information about its
  subtree

10
Mobile Applications

• Information Services (Yellow Pages)
• Law Enforcement and Medical Emergencies
• Sales and Mobile Offices
• Weather, Traffic, Sports, Entertainment
• Trucking
• Cellular Subscribers in the United States
• 90,000 in 19844.4 million in 199013 million in
  1994
• Handheld computer market will grow to 1.77
  billion by 2002

11
Technology Push

• Internet ftp, telnet, email, http,html
• Advancing Wireless Communication Technologies
• Laptop, Notebook, and Palmtop Computers

12
Classification of Mobile Database Systems
13
Data Management Issues

• Speed of wireless link
• Scalability
• Mobility
• Location dependent data Location specific
  queries
• Limited by battery power
• Disconnection (Voluntary, Involuntary)
• Replication/Caching
• Handoff

14
Insurance Example
15
Medical Example

• 911 Call
• Ambulance arrives/departs
• Closest hospital
• Access patient records
• Send vital signs
• Update patient records
• Page hospital personnel
• Order medical supplies

16
MC/DB Research

• Transaction Processing
• Caching - Replication
• Broadcast Disks
• Agents
• Mobility
• Location Dependent Data
• Recovery
• ACID (?)

17
Outline

• Introduction Data Management Issues
• Query ProcessingLocation Dependent Queries and
  DataNew Query TypesQuery Optimization
• Data Broadcasting
• Transaction Processing
• Projects Products
• Conclusion

18
Location Dependent Data

• Value of data depends on location
• Temporal Replication - One consistent value at
  one time
• Spatial Replication - Multiple different correct
  data values at one time
• Temporal Consistency - All data objects satisfy a
  given set of integrity constraints.
• Spatial Consistency - Consistency constraints
  satisfied within Data Region.
• SMU/University of Missouri at Kansas City, 17

19
Location Dependent Queries

• Result depends on location
• Different from traditional distributed goal of
  location independence
• Ex Yellow Pages, Directions, Map
• Predicates based on location Find the cheapest
  hotel in Dallas.
• Location constraints Find the nearest hotel
  (to me).

20
Similarity to Spatial Queries

• Spatial Data Data associated with space occupied
  by object.
• Types of spatial queries contains, contained
  in, intersects, neighboring, east of, etc.
• Spatial data structures
• Spatial operators
• Spatial selects and joins
• PSQL - extend SQL, 18,20

21
Differences from Spatial Queries

• Client is actually moving
• Location of client may be part of the query
  itself
• May depend on direction of movement
• Data may not directly contain location
  information
• Includes temporal features as well

Spatial data is dynamic
22
Querying Moving Objects

• Moving Objects Spatio-Temporal (MOST) data model
• Dynamic Attributes - Change over time
• Queries over temporal history
• Instantaneous - Ex Find all restaurants Ill
  reach in the next half hour.
• Continuous - Ex Find all restaurants within 5
  miles. The answer continuously changes as the
  MU moves.
• Persistent - Ex Find the cars that travel
  greater than 10 miles in the next half hour.
• Future Temporal Logic (FTL) language
• University of Illinois, 20

23
Query Optimization

• How best to satisfy the information request made
  by the client?
• Different Cost Factors I/O, network
• Different Access Options cache, FN, broadcast
• Dynamic and Adaptable - environment changes
• Alternative plans include deciding (based on
  state of MH and environment) whether to access in
  the cache at the MH, to request a mobile
  transaction, or to obtain from a broadcast disk.

24
Outline

• Introduction Data Management Issues
• Query Processing
• Data BroadcastingOverviewIndexingResearch
• Transaction Processing
• Projects Products
• Conclusion

25
Data Broadcasting

• Server continually broadcasts data to MUs.
• Scalability Cost does not depend on number of
  users listening.
• Mobile Unit may/may not have cache.
• Facilitates data access during disconnected
  periods.
• Allows location dependent data access.
• No need to predict with 100 accuracy the future
  data needs.
• Broadcast based on probability of access.
• Periodic broadcasting of all data.

26
Data Broadcasting (contd)

• Classification
• Coverage - Everything, Subset
• Content - Static, Dynamic
• Indices - Index, Self Descriptive
• Data Stream - Flat, Skewed, Multiple Disks
• Client - Passive, Active
• For uniform page access, flat disk has best
  expected performance.
• With skewed page access, nonflat disks are
  better.
• Push based.

27
Broadcast Disks

• Simulate multiple disks of varying sizes and
  speeds. Data of higher interest on smaller
  faster disks (broadcast more frequently).
• Each disk contains data with similar access
  behavior.
• Combination of caching and broadcast disks.

Figure 4.1 from 15
28
Broadcast Disks (contd)

• Dont want to store hottest pages. They may be
  broadcast frequently.
• Store in cache if probability of access (P) is
  greater than the frequency of broadcast (X).
• Cost based page replacement.
• Replace cache page with smallest P/X - PIX. Too
  expensive to implement.
• LIX - PIX approximation. Works well particularly
  with noise.
• Brown, MITL, Maryland, 37,38,39

29
Air-Cache

• Dynamic - Adapts to system workload.
• Define temperature of data
• Vapor (Steamy) Hot - Accessed frequently and
  broadcast.
• Liquid Warm - Accessed often, not broadcast, but
  kept in servers main memory.
• Frigid (Icy) Cold - Accessed infrequently and
  stored on secondary storage.

30
Air-Cache (contd)

• Three level memory hierarchy based on
  temperature.
• Sparks (access) to data can increase temperature.
  No sparks, results in a reduction of
  temperature.
• Simulation results predict very good performance.
• Maryland, 43

31
Adaptive Protocols

• Dynamically modify broadcast contents.
• Constant Broadcast Size (CBS) Server Protocol
• Limited size and periodic
• Priority
• Popularity Factor (PF)
• Ignore Factore (IF)
• Variable Broadcst Size (VBS) Server Protocol
• Aperiodic
• All data above threshold PF included.
• Arizona and UMKC, 40

32
Outline

• Introduction Data Management Issues
• Query Processing
• Data Broadcasting
• Transaction ProcessingOverviewTransaction
  ModelConcurrencyRecoveryResearch
• Projects Products
• Conclusion

33
Mobile Transaction (MT)

• Database transaction requested from a MU. May
  execute in FN or MU
• Issues
• Disconnect/Handoff
• Mobility
• Location Dependent Data
• Error Prone
• MU Resources/ Power
• Recovery/Restart
• Management

34
MT Requirements

• Keep autonomy of local DBMS
• LLT
• Interactive
• Advanced transaction models
• Nested
• Multidatabase
• Request from MU
• Execute anywhere
• Capture movement
• ACID (?)

35
MT Approaches

• No consensus on accepted approach
• MU may not have primary copy of data 45
• Transaction Proxy MU does no transaction
  processing
• Read Only Transaction MU only reads data
• Weak Transaction Read and update cached data
  Must synchronize updates with primary copy on FN.
• MU may have primary copy of data
• MU may access data on other MUs
• First class and second class transactions

36
MT Recovery

• Transaction, site, media, network failure - More
  frequent than in wired network.
• Different types of failures (partial)
• Handoff
• Voluntary disconnection
• Battery problems
• Lose computer??
• Checkpoint data at MU to BS
• Checkpoint at handoff
• Database log plus transaction log
• May need compensating transactions

37
Atomicity for MT

• Weaken or provide different types of atomicity
• May decompose transaction into subtransactions
• May require atomicity at lower than transaction
  level
• Atomic commitment difficult (expensive)
• Global commit/Local Commit

38
Consistency for MT

• Weakening isolation and atomicity may weaken this
  as well.
• May divide data into clusters with consistency
  within clusters.
• Reintegration of updates after reconnect may
  cause many conflicts.
• May use bounded inconsistency.
• Impacted by location dependent data

39
Isolation for MT

• May be too restrictive
• Cant always do at MU (disconnection)
• Isolation at lower levels in transaction
• Commitment at different levels of transaction
• Cooperating transactions

40
Durability for MT

• Durability for partial results
• May want durability for parts of transactions.
• Due to conflicts at reconnect, even durability of
  subtransactions may not be guaranteed.
• Local commit vs.. Global commit

41
MT Concurrency Control

• Mobility of MUs may increase message traffic for
  lock management
• MU failure may leave some data locked /unlocked

6) T1 Unlock(Xa) Commit
Fig 2 from 48
42
Revised Optimistic Locking

• O2PL-MT
• Read locks may be executed at multiple servers.
• Read unlock can be executed at any site
• Benefit shown using analytic model
• Purdue, 48

Figure 3 from 48
43
Kangaroo Transaction (KT)

• Built on top of global transactions
• Captures data and movement behavior
• DAA as BS - Maintains logging and transaction
  status
• Logging at BS
• Flexible atomicity
• Restart after disconnect
• Management moves

44
Kangaroo Transaction (contd)

• Local Transaction - Sequence of read and write
  operations ending in commit or abort
• Global Transaction - Sequence of global or local
  transactions
• Joey Transaction - Sequence of global and local
  transactions ending in commit, abort, or split
• Kangaroo Transaction - Sequence of one or more
  Joeys with last one ending in commit or abort.
  All earlier end in split
• SMU, 47

45
KT and Movement
46
Reporting and Co-Transactions

• Mobile transaction is a special type of
  multidatabase transactions.
• GDMS exists at each base station.
• Subtransactions of the mobile transaction will
  commit or abort independently.
• Atomic and non-compensatable transactions.
• Reporting and co-transactions.
• Pittsburgh, 46

47
Clustering Model

• Views mobile transaction as beginning on mobile
  and nonmobile hosts.
• Transaction migration
• Transaction model is designed to maintain
  consistency of the database.
• Database is divided into clusters.
• Data is divided into core and quasi copies.
• Mobile transactions and operations are decomposed
  into a set of weak and strict transactions.

48
Clustering Model (contd)

• Weak operations access only data in the same
  cluster. Strict operations allowed database wide
  access. Two copies of data can be maintained
  (strict and weak).
• Clusters defined based on location and user
  profile.
• Transaction Proxy dual transaction of one
  executed at mobile host which includes only the
  updates.
• Purdue, 51,52

49
Mobile Transactions and Ambulatory Care

• Medical Personal Digital Assistant (MPDA)
• Battlefield - Cache copy of soldiers medical
  records in MPDA
• Distributed Medical Database - EMT obtains
  patients medical record and updates.
• BSA (Base Station Agent) is responsible for
  logging and recovery.
• Recovery based on sagas with save-points.
• Mailboxes used to save information.
• Purdue, 49,50

50
Semantics-Based Mobile Transaction Processing

• Views mobile transaction processing as a
  concurrency and cache coherency problem.
• A stationary database server dishes out the
  fragments of an object on a request from a Mobile
  Unit.
• On completion of the transaction, the Mobile
  Units return the fragments to the server.
• These fragments are put together again by the
  merge operation at the server.
• Pittsburgh, 54

51
Multidatabase Transaction Processing Manager

• Mobile transactions built on top of multidatabase
  global transactions.
• Timestamps used to enforce ordering
• Allows voluntary disconnections.
• MU part of MDS
• Message Queuing Facility (MQF)
• MU sends request to designated coordinating node
  on FN.
• Monash, 56

52
PRO-MOTION

• MC/Database Transaction Processing approach
• Multiple transaction types
• Controlled divergence
• ACID
• Update cache and later DB at FH
• Compact - Compact Agent at MU, Mobility Manager
  at BS, Compact Manager at Server
• Pittsburgh, 55

53
MT Research Limitations

• Architectural Assumptions
• No support for location dependent data
• Few Implementations

54
MT Management Options

• MU
• BS
• Combination
• Fixed/Relocatable/Moving
• Agent

55
Outline

• Introduction Data Management Issues
• Query Processing
• Data Broadcasting
• Transaction Processing
• Projects Products
• Conclusion

56
Some DB/MC Projects URLs

• MobiDick - Monash Univ. (Australia)
  http//www.ct.monash.edu.au/mobidick
• Mobisaic - Univ. of Washington
  http//www.cs.washington.edu/homes/voelker/mobisai
  c
• Purdue http//www.cs.purdue.edu/research/cse/mobi
  le
• SMU http//www.seas.smu.edu/mhd/mobile.html
• MCC - Collaboration Managment Infrastructure
  http//www.mcc.com/projects/transaction
• University of Ioanina http//zeus.cs.uoi.gr/
• Michigan - CITI http//www.citi.umich.edu/project
  s/mobile.html
• UCLA - Ficus http//ficus-www.cs.ucla.edu/ficus
• Columbia http//www.mcl.cs.columbia.edu

57
Rover

• Figure 6.1 from 15

58
Oracle Mobile Agent
Message Manager

• Commercial Product
• Application, Static, Multiple
• Message Manager - MU
• Message Gateway - BS
• Agent - FN (Server)
• 67,69

Gateway
Corporate Network
Agent
Database Server
59
Sybase - SQL Anywhere

• Designed for Windows, (95, 3.x, NT), OS/2, DOS
• Limited memory requirements
• Full TP capabilities
• Includes SQL Remote
• Compatible with Sybase SQL Server
• 68

60
Sybase (contd) - SQL Remote

• Two way replication based on message passing.
• Remote database are synchronized with
  consolidated DB
• Message Agent required at DB server
• Replication of subscribed fragments
• Periodic changes sent from consolidated DB to
  remote DBs
• Updates from committed transactions at remote
  submitted to consolidated database.
• Conflicts Consolidated is master Triggers used.

61
Informix

• I-Mobile 1.0 discontinued
• No replication
• Three tier approach appropriate for long term,
  but in the short term users wanted to be able to
  use existing client-server applications (not
  rewrite).
• Small DBMS server to run on mobile client
• Only dial up needed for now
• Informix Dynamic Server/Personal Edition (IDS/PE)
  for Windows 95/NT. Mobiles and desktop clients
• 64,66

62
Outline

• Introduction Data Management Issues
• Query Processing
• Data Broadcasting
• Transaction Processing
• Products
• Conclusion

63
Future

• Combine different approaches
• Semantic caching
• Query Optimization
• Adaptive Data Broadcasting
• Performance Benchmarks
• Security
• Location Dependent Queries

64
Acknowledgements and URL Bibliographies

• Earlier version of this tutorial presented at the
  1996 Brazilian Database Symposium.
• We particularly want to thank Evaggelia Pitoura
  for providing several tables and figures from her
  recent book 15.
• Some slide information obtained from slides
  presented at a database class at the University
  of Massachusetts, http//www-ccs.cs.umass.edu/mobi
  le.
• Online bibliographies
• http//www.seas.smu.edu/mhd/mobile.html
• http//www.ct.monash.edu.au/mobidick