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Transcript of IEEE ICDE 98 Tutorial: Mobile Computing and Databases Margaret H. Dunham Southern Methodist...
![Page 1: IEEE ICDE 98 Tutorial: Mobile Computing and Databases Margaret H. Dunham Southern Methodist University Dept of Computer Science and Engineering Dallas,](https://reader035.fdocuments.us/reader035/viewer/2022062620/5519d6ea5503468b0c8b49e5/html5/thumbnails/1.jpg)
IEEE ICDE ‘98 Tutorial:Mobile Computing and Databases
Margaret H. Dunham
Southern Methodist University
Dept of Computer Science and Engineering
Dallas, Texas 75275
http://www.seas.smu.edu/~mhd
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Outline
Introduction & Data Management IssuesQuery ProcessingCachingData BroadcastingTransaction ProcessingAgentsProjects & ProductsConclusion
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Mobile Computing Architecture
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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 moveMobile Host (MH) (Mobile Unit (MU))
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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
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Wireless Networks (cont’d)
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)
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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 BSsHard - Only connected to one BS
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Location Management
Tracking mobile userUser associated with home location
server (Home Agent)May augment by searching in local
area firstMay augment with user profilesMobile IP [11,14]
Triangle Routing Route Optimization Location Control (Routing Agent)
M
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Location Management (cont’d)
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
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Mobile Applications
Information Services (Yellow Pages)Law Enforcement and Medical EmergenciesSales and Mobile OfficesWeather, Traffic, Sports, EntertainmentTruckingCellular Subscribers in the United States:
90,000 in 1984;4.4 million in 1990;13 million in 1994
Handheld computer market will grow to $1.77 billion by 2002
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Technology Push
Internet: ftp, telnet, email, http,htmlAdvancing Wireless Communication
TechnologiesLaptop, Notebook, and Palmtop Computers
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Classification of Mobile Database Systems
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Data Management Issues
Speed of wireless linkScalabilityMobility Location dependent data; Location specific queriesLimited by battery powerDisconnection (Voluntary, Involuntary)Replication/CachingHandoff
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Insurance Example
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Medical Example
911 CallAmbulance arrives/departsClosest hospitalAccess patient recordsSend vital signsUpdate patient recordsPage hospital personnelOrder medical supplies
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MC/DB Research
Transaction ProcessingCaching - ReplicationBroadcast DisksAgentsMobilityLocation Dependent DataRecoveryACID (?)
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Outline
Introduction & Data Management Issues Query Processing
Location Dependent Queries and DataNew Query TypesQuery Optimization
CachingData BroadcastingTransaction ProcessingAgentsProjects & ProductsConclusion
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Location Dependent Data
Value of data depends on locationTemporal Replication - One consistent value at one
timeSpatial Replication - Multiple different correct data
values at one timeTemporal 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]
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Location Dependent Queries
Result depends on locationDifferent from traditional distributed goal of
location independenceEx: Yellow Pages, Directions, MapPredicates based on location: “Find the
cheapest hotel in Dallas.”Location constraints: “Find the nearest hotel (to
me).”
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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 structuresSpatial operators Spatial selects and joinsPSQL - extend SQL, [18,20]
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Differences from Spatial Queries
Client is actually movingLocation of client may be
part of the query itselfMay depend on direction of movementData may not directly contain location
information Includes temporal features as well
Spatial data is dynamic
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Querying Moving Objects
Moving Objects Spatio-Temporal (MOST) data model Dynamic Attributes - Change over time Queries over temporal history:
Instantaneous - Ex: “Find all restaurants I’ll 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]
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Query Optimization
How best to satisfy the information request made by the client?
Different Cost Factors: I/O, networkDifferent Access Options: cache, FN, broadcastDynamic and Adaptable - environment changesAlternative 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.
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Outline
Introduction & Data Management IssuesQuery Processing Caching
OverviewTypes
Research Data BroadcastingTransaction ProcessingAgentsProjects & ProductsConclusion
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Caching
Placing data at MU. Usually on disk.Faster to access from MU than from DBMS in
fixed network.Facilitates disconnected operation.Adaptive to connection mode.Not just another replicaPull basedMost work on files not databases
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Caching Functions
Data fetching Granularity (Page, file, table, semantic)
ReplacementCoherency
Callback - Servers send invalidation messages to clients.
Detection - Clients send queries to servers to.
Updating during disconnectData integration when reconnected
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Connectivity and File Systems
Table 3.2 from [15]
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MU Replica Control Protocols
Traditional Replication Protocol problems: May hinder mobility Quorum Consensus: Can’t get quorum if
disconnected; Avoid using MU replicas to make up quorum
Location information not always readily availablePrimary Copy: Should not be stored at MUFirst class/Second class replicas
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Checkpointing
Table 3.4 from [15]
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Prefetching vs.. Hoarding
Both prefetch data in anticipation of future use.Prefetching
Objective is to improve performance (throughput or response time).
Cache miss not catastrophic.
Hoarding Objective is to fetch all needed data into MU cache prior to
disconnect. Thus the goal is to facilitate disconnected operation.
Cache miss is catastrophic. OK to overfetch
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Hoarding/Spying
Listening to and recording file accessesPerformed during a snapshot intervalMay be combined with user profiles. Results limited to the snapshot.
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Disconnected Issues
Table 3.1 from [15]
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Coda
First project to demonstrate disconnected operation.Optimistic LockingGranularity - sets of files.Coherency - callbacksHoard Walking: Periodically (every 10 min) evaluate
contents of cache. Recalculate priorities.On a callback break, object is purged, refetching on
demand or during next hoard walk.Venus - cache manager at MU
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Coda (cont’d)
Venus states: hoarding,emulating,write disconnected (earlier reintegrating).
Cache misses during disconnection are treated as failures.
During disconnection, a log (Change Modify Log) of operations is created.
Hoarding
WriteDisconnected
Emulating
Adapted from Fig 2 in [34]
Disconne cti on
Strong Connection
Weak Connection
Conne
ction
Discon
necti
on
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Coda (cont’d)During integration, log applied. Conflicting updates are
determined and user assists in resolutionTimestamps at volume and object level used to determine
conflicts.Trickle Reintegration used to asynchronously propagate
updates. Hoard Profile - list of files and priorities.Lowest priority objects chosen for replacement.Weak Connectivity - low bandwidth, high latency, high cost, or
intermittentCMU, [29,34]
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Little Work
Disconnected AFSCache operations depends on type of connection
Connected - Continuous; High bandwidth; Normal operation
Partially Connected - Continuous; Dialup; Delayed writes
Fetch Only - On demand; Cellular; Optimistic replication
Disconnected - Fail if cache miss
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Little Work (cont’d)
Caches 64KB chunks of files Fetch only modeModifications sent back to primary file serverConflicts stored separately and user notifiedMichigan, [25,26]
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Seer
FicusUses semantic information to determine contents of
cache.Semantic distance between files measured in number
of file accesses on average between two files.Access is defined as open-close.Distance measure used to cluster files. Fetching of a
cluster based on user hints and LRU information.UCLA, [24,30]
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Summary
Table 3.1 from [15]
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Sleepers and Workaholics
Cache invalidation reportPeriodically (synchronous) the server broadcasts
report of changed data.MU waits for next report prior to answering query.Sleepers - frequently disconnected; cache invalidation
based on signatures.Workaholics - rarely disconnected; periodic broadcast
of changes.False InvalidationMITL and Rutgers, [21]
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Transparent Analytic Spying
File Working SetsContinually observe and
record (in a log) file access. At hoard time, reference the log to determine hoard.
Trees for a process are created reflecting file access pattern. One tree per program execution is generated.
A
B C
D E F
Access Tree
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Transparent Analytic Spying (cont’d)
Hoard all files or only those in in the most recent execution.
Tracing adds about 2% CPU overhead. Average space for file log record is 100 bytes.
Implemented on Unix, NFS, MachCache miss rate over wireless slightly higher than on
wired.Prefetching overall reduced cache misses and
elapsed timeColumbia, [36]
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Predictive File Caching
Analyze file access patterns in different environments: Personal productivity, Programming, Commercial
Working Set Statistics: Mean working-set sizes small (18MB per day)
Attention Shift Statistics: 0.6 per user per weekConflict Statistics: Depends on environmentConclusion:
Hoarding is possible due to small working set size LRU caching insufficient
UCLA, [31]
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Virtual Primary Copy
Mobile Primary Copy (MPC) at MUVirtual Primary Copy (VPC) at BSGlobal transactions access VPCConsistency of VPC maintained by BSBS monitors MU disconnectMultilayered approach is transparent to other
sites Monash, [23]
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Roam
MC Replication SystemMU Peer to Peer communication
allowedWard Model:
Ward: Grouping of replicas for locations that frequently communicate
Ward Set: Set of replicated data stored in a ward.
Ward Master: Doorway into ward. Maintains consistency between wards.
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Roam (cont’d)
Ward members are “close”No “pre-motion” operations Intra-ward synchronization easier than inter-
wardReconciliation- Synchronization processSelective replication at file levelScales wellUCLA, [33]
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Semantic Cache
Caching granularity at a predicate levelSPJ query - Materialized viewAdvantages: reduces network overhead,
reduces cache spaceDisadvantages: Indexing, query trimmingSemantic Cache - C = {Si}Semantic Segment - Si=<Sr,Sa,Sp,Sc>SMU
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Outline
Introduction & Data Management IssuesQuery ProcessingCaching Data Broadcasting
OverviewIndexingResearch
Transaction ProcessingAgentsProjects & ProductsConclusion
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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.
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Data Broadcasting (cont’d)
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.
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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]
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Broadcast Disks (cont’d)
Don’t 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]
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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 server’s main memory.
Frigid (Icy) Cold - Accessed infrequently and stored on secondary storage.
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Air-Cache (cont’d)
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]
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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]
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Outline
Introduction & Data Management Issues Query Processing Caching Data Broadcasting
Transaction ProcessingOverviewTransaction ModelConcurrencyRecoveryResearch
Agents Projects & Products Conclusion
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Mobile Transaction (MT)
Database transaction requested from a MU. May execute in FN or MU
IssuesDisconnect/HandoffMobilityLocation Dependent DataError ProneMU Resources/ PowerRecovery/Restart Management
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MT Requirements
Keep autonomy of local DBMSLLT InteractiveAdvanced transaction models
Nested Multidatabase
Request from MUExecute anywhereCapture movementACID (?)
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MT Approaches
No consensus on accepted approachMU 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 dataMU may access data on other MUsFirst class and second class transactions
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MT RecoveryTransaction, 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 BSCheckpoint at handoffDatabase log plus transaction logMay need compensating transactions
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Atomicity for MT
Weaken or provide different types of atomicityMay decompose transaction into
subtransactionsMay require atomicity at lower than transaction
levelAtomic commitment difficult (expensive)Global commit/Local Commit
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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
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Isolation for MT
May be too restrictiveCan’t always do at MU (disconnection) Isolation at lower levels in transactionCommitment at different levels of transactionCooperating transactions
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Durability for MT
Durability for partial resultsMay want durability for parts of transactions.Due to conflicts at reconnect, even durability of
subtransactions may not be guaranteed.Local commit vs.. Global commit
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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;
Server ACell A
XaYa
Server BCell B
XbYb
Server CCell C
XcYcZc
1) T1: Lock(Xa); Read(Xa)
2) T1 moves to B
3) T1: Lock(Yb); Read(Yb)
4) T1 moves to C
5) T1: Lock(Zc); Write(Zc);
Unlock(Zc); Commit
6) T1: Unlock(Yb); Commit;
Fig 2 from [48]
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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]
LOCK HELD
LOCKREQUEST
W_INTEND R_LOCK W_LOCK
W_Intend No Yes No
R_Lock No Yes No
W_Lock No No No
Figure 3 from [48]
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Kangaroo Transaction (KT)
Built on top of global transactionsCaptures data and movement behaviorDAA as BS - Maintains logging and transaction
statusLogging at BSFlexible atomicityRestart after disconnectManagement moves
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Kangaroo Transaction (cont’d)
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]
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KT and Movement
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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]
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Clustering Model
Views mobile transaction as beginning on mobile and nonmobile hosts.
Transaction migrationTransaction 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.
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Clustering Model (cont’d)
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]
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Mobile Transactions and Ambulatory Care
Medical Personal Digital Assistant (MPDA)Battlefield - Cache copy of soldiers’ medical records in
MPDADistributed Medical Database - EMT obtains patient’s
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]
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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]
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Multidatabase Transaction Processing Manager
Mobile transactions built on top of multidatabase global transactions.
Timestamps used to enforce orderingAllows voluntary disconnections.MU part of MDSMessage Queuing Facility (MQF)MU sends request to designated coordinating
node on FN.Monash, [56]
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PRO-MOTION
MC/Database Transaction Processing approachMultiple 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]
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MT Research Limitations
Architectural AssumptionsNo support for location dependent dataFew Implementations
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MT Management Options
MUBSCombinationFixed/Relocatable/Moving
Agent
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Outline
Introduction & Data Management IssuesQuery ProcessingCachingData BroadcastingTransaction Processing Agents
OverviewClient-Agent-Server ModelMobile Agent
Projects & ProductsConclusion
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Agent
Application dispatched by and working for the client.
Agent: Solves disconnect problem Solves slow bandwidth problem
Client-Agent-ServerAgent Classification:
Type - Client,Server,Application,UserMovement - Static, Relocatable, Migrating (Mobile)Number - Single, Multiple, Clonable
Client Server
Client Agent Server
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Itinerant Agent (Mobile Agent)
Program dispatched from mobile unit that roams through the fixed network to satisfy client’s data request.
At a server the agent is sent to an Agent Meeting Point (AMP) where desired server functions are determined and requested.
Client, Migrating, Single IBM, [58]
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Migrating User Agent
User process that mimics MU.Process migrates as user moves.Client, Migrating, SingleMassachusetts, AT&T, [63]
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Remote Programming
Language for communication required.User and server communication without using
network.Places - Meeting points for agents and serversAgents - Application is set of agents. Agent is
either at a place or travelling between places.Travel - Go instructionMeetings - Agents communicating at a place.General Magic Telescript, [59]
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Concordia
Mitsubishi Electra ITAJava Objects; JDBCCollaborating AgentsAgent Server - FNUser Agent - MU to BSQuery Agents- BS to ServerCollaborator - BSMitsubishi Electric ITA,
[60,61] Adapted from Fig 6 in [61]
UserAgent
Query Agent
Query Agent
Collaboration
OracleServer
NotesServer
Concordia
Concordia
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Outline
Introduction & Data Management IssuesQuery ProcessingCachingData BroadcastingTransaction ProcessingAgents Projects & Products
Conclusion
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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/mobisaic Purdue; http://www.cs.purdue.edu/research/cse/mobile 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/projects/mobile.html UCLA - Ficus; http://ficus-www.cs.ucla.edu/ficus Columbia; http://www.mcl.cs.columbia.edu
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Rover
Figure 6.1 from [15]
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Oracle Mobile Agent
Commercial ProductApplication, Static,
MultipleMessage Manager - MUMessage Gateway - BSAgent - FN (Server) [67,69]
Message Manager
Gateway
Corporate Network
Agent
Database Server
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Sybase - SQL Anywhere
Designed for Windows, (95, 3.x, NT), OS/2, DOS
Limited memory requirements
Full TP capabilities Includes SQL RemoteCompatible with Sybase
SQL Server [68]
Remote DatabaseSQL Anywhere standalone engine
Message agent
Consolidated DatabaseSQL Anywhere network server
Message agent
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Sybase (cont’d) - SQL RemoteTwo way replication based on
message passing.Remote database are synchronized
with consolidated DBMessage Agent required at DB serverReplication of subscribed fragmentsPeriodic changes sent from consolidated
DB to remote DBsUpdates from committed transactions at remote submitted to
consolidated database. Conflicts: Consolidated is master; Triggers used.
ConsolidatedDB
Remote Databases
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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]
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Outline
Introduction & Data Management IssuesQuery ProcessingCachingData BroadcastingTransaction ProcessingAgentsProducts Conclusion
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Future
Combine different approachesSemantic cachingQuery OptimizationAdaptive Data BroadcastingPerformance BenchmarksSecurityLocation Dependent Queries
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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/mobile.
Online bibliographies http://www.seas.smu.edu/~mhd/mobile.html http://www.ct.monash.edu.au/~mobidick