6/30/2015 01:07 1 mobile and wireless database access for pervasive computing panos k. chrysanthis...
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Mobile and Wireless Database Access for Pervasive Computing
Panos K. ChrysanthisUniversity of Pittsburgh & Carnegie Mellon University
Evaggelia Pitoura University of Ioannina
[email protected] [email protected]
An IEEE ICDE 2000 Tutorial on
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Outline
Motivating Example Issues: Mobility, Wireless Communication, Portability Adaptability and Mobile Client-Server Models Location Management Broadcast data dissemination Disconnected database operations Mobile Access to the Web Mobility in Workflow Systems State of Mobile DB Industry and Research Projects Unsolved Problems
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Party on Friday
Update Smart Phone’s calendar with guests names.
Make a note to order food from Dinner-on-Wheels.
Update shopping list based on the guests drinking preferences.
Don’t forget to swipe that last can of beer’s UPS label.
The shopping list is always up-to-date.
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Party on Friday
AutoPC detects a near Supermarket that advertises sales.
It accesses the shopping list and your calendar on the Smart Phone. It informs you the soda and beer are on sale, and reminds you.
that your next appointment is in 1 hour. There is enough time based on the latest traffic report.
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Party on Friday
TGIF… Smart Phone reminds you that you need to order food by
noon. It downloads the Dinner-on-Wheels menu from the Web
on your PC with the guests’ preferences marked. It sends the shopping list to your
CO-OP’s PC. Everything will be delivered by the time
you get home in the evening.
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Mobile Applications
Expected to create an entire new class of Applications new massive markets in conjunction with the Web Mobile Information Appliances - combining personal
computing and consumer electronics Applications:
Vertical: vehicle dispatching, tracking, point of sale Horizontal: mail enabled applications, filtered
information provision, collaborative computing…
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Mobile and Wireless Computing
Goal: Access Information Anywhere, Anytime, and in Any Way.
Aliases: Mobile, Nomadic, Wireless, Pervasive, Invisible, Ubiquitous Computing.
Distinction:• Fixed wired network: Traditional distributed computing.• Fixed wireless network: Wireless computing.• Wireless network: Mobile Computing.
Key Issues: Wireless communication, Mobility, Portability.
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Wireless Communication
Cellular - GSM (Europe+), TDMA & CDMA (US)– FM: 1.2-9.6 Kbps; Digital: 9.6-14.4 Kbps (ISDN-like
services) Public Packet Radio - Proprietary
– 19.2 Kbps (raw), 9.6 Kbps (effective) Private and Share Mobile Radio Wireless LAN - wireless LAN bridge (IEEE 802.11)
– Radio or Infrared frequencies: 1.2 Kbps-15 Mbps Paging Networks – typically one-way communication
– low receiving power consumption Satellites – wide-area coverage (GEOS, MEOS, LEOS)
– LEOS: 2.4 Kbps (uplink), 4.8Kbps (downlink)
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Mobile Network Architecture
FIXED NETWORK
PDA
FIXEDHOSTBASE
STATION
BASESTATION
BASESTATION
Mbps to Gbps
MOBILE HOST
WIRELESS LAN CELL2Kbps - 15Mbps
WIRELESS RADIO CELL9Kbps - 14Kbps
BASESTATION
PDA
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Wireless characteristics
Variant Connectivity Low bandwidth and reliability
Frequent disconnections • predictable or sudden
Asymmetric Communication Broadcast medium
Monetarily expensive Charges per connection or per message/packet
Connectivity is weak, intermittent and expensive
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Portable Information Devices
PDAs, Personal Communicators Light, small and durable to be easily carried around dumb terminals [InfoPad, ParcTab projects],
palmtops, wristwatch PC/Phone, walkstations
will run on AA+ /Ni-Cd/Li-Ion batteries may be diskless I/O devices: Mouse is out, Pen is in wireless connection to information networks
either infrared or cellular phone specialized HW (for compression/encryption)
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Portability Characteristics
Battery power restrictions transmit/receive, disk spinning, display, CPUs,
memory consume power Battery lifetime will see very small increase
need energy efficient hardware (CPUs, memory) and system software
planned disconnections - doze mode
Power consumption vs. resource utilization
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Portability Characteristics
Resource constraints Mobile computers are resource poor Reduce program size – interpret script languages
(Mobile Java?) Computation and communication load cannot be
distributed equally Small screen sizes
Asymmetry between static and mobile computers
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Mobility Characteristics
Location changes• location management - cost to locate is added to
communication Heterogeneity in services
bandwidth restrictions and variability Dynamic replication of data
• data and services follow users Querying data - location-based responses Security and authentication System configuration is no longer static
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What Needs to be
Reexamined?
Operating systems File systems Data-based systems Communication architecture and protocols Hardware and architecture Real-Time, multimedia, QoS Security Application requirements and design PDA design: Interfaces, Languages
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Query/Transaction
Processing
Concern moves from CPU time and network delays to battery power and communication costs (including tariffs)
Updates may take the form of long-running transactions nodes may continue in disconnected mode need new transaction models [Chrysanthis 93, Satya 94]
Move data vs. move query/transaction Context (location) based query responses Consistency, autonomy, recovery
Approximate answers Stable storage for logs, data -- stabilize at servers?
Providing uniform access in a heterogeneous environment Design of human-computer interfaces (pen-based computing) Updated system info: Location information, user profiles
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Recurrent Themes
Handling disconnections (planned failures?) caching strategies managing inconsistencies
Delayed write-back and prefetch: use network idle times increases memory requirements
Buffering/batching: allows bulk transfers Partitioning and replication
triggered by relocation Compression: increase effective BW
increases battery power requirements Receiving needs less power than sending
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Outline
Motivating Example Issues: Mobility, Wireless Communication, Portability Adaptability and Mobile Client-Server Models Location Management Broadcast data dissemination Disconnected database operations Mobile Access to the Web Mobility in Workflow Systems State of Mobile DB Industry and Research Projects Unsolved Problems
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Mobility in Db Applications
• Need to adapt to constantly changing environment:
• network connectivity
• available resources and services
• By varying and (re)negotiating:
• the partition of duties between the mobile and static elements
• the quality of data available at the mobile host
Example: Fidelity (degree to which a copy of data matches the reference copy at the
server)
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Laissez-Faire Application Transparent
Application-Aware
(+) existing applications continue to work unchanged
(-) too general, cannot take advantage application semantics
(-) may not be attainable (e.g., during a long disconnection)
(-) applications must be re-written which may be very complicated
(-) no focal point of control to resolve potentially incompatible application demands or to enforce limits on resource usage
Adaptability
Where should support for mobility and adaptability be placed?
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Adaptive Applications
Need: Measurement of QoS and communication with
application– A mechanism to monitor the level and quality of information
and inform applications about changes. Programmer Interface for Application-Aware
Adaptation – Applications must be agile: able to reveive events in an
asynchronous manner and react appropriately A central point for managing resources and authorizing
any application-initiated request.
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Client ServerAgent
Fixed NetworkWireless Link
C-SA-C: Server-side Agent
C-SA-C: The Client/Server-side Agent/Server Model Splits the interaction between the mobile client and
server: client-agent and agent-server
• different protocols for each part of the interaction
• each part may be executed independently of the other
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Responsibilities of the Agent
Messaging and queying Manipulate data prior to their transmission to the
client: perform data specific compression batch together requests change the transmission order
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Role of the Agent
Surrogate or proxy of the client Any communication to/from the client goes through the
agent Offload functionality from the client to the agent
Application (service) specific provides a mobile-aware layer to specifc services or
applications (e.g., web-browsing or database access) handles all requests from mobile clients
Filters provide agents that operate on protocols E.g., an MPEG-agent or a TCP-agent
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C-CA-S: Client-side Agent
C-SA-S: The Client/Client-side Agent/Server Model caching background prefetching and hoarding various communication optimizations
Mobile Host
Client ServerAgent
Fixed NetworkWireless Link
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Mobile Host
Client ServerAgent
Fixed Network
Wireless Link
Agent
C-I-S: Client & Server Agents
C-I-S: Client/Intercept/Server Model Caching, prefetching etc various communication optimizations at both ends
– E.g., asynchronous queued RPC relocate computation between the agents Client interoperability
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Mobile Agents
Mobile agents are migrating processes associated with an itinerary dynamic code and state deployment
Implement the agents of the previous architectures as mobile agents, E.g., server-side agents can relocate during handoff client-side agent dynamically move on and off the client
– Relocatable dynamic objects (RDO) [Rover] Implement the communication using mobile agents:
clients submit/receive mobile agents to/from the server E.g., Compacts [Pro-Motion]
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A Taxonomy
C-CA-S
C-SA-S
C-I-S
C-CA-MS
C-SA-MS
C-I-MS
Coda
WirelessWeb Browser
Pro-motionRover
WebExpress
Pro-motionOracle mobile
agents
Odyssey
Adaptability
Strategy
Laissez-Faire ApplicationMobilityAware
ApplicationTransparent
Unm
odifi
ed S
erve
rM
odifi
ed S
erve
r
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Outline
Motivating Example Issues: Mobility, Wireless Communication, Portability Adaptability and Mobile Client-Server Models Location Management Broadcast data dissemination Disconnected database operations Mobile Access to the Web Mobility in Workflow Systems State of Mobile DB Industry and Research Projects Unsolved Problems
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Locating Moving Objects
Example of moving objects mobile devices (cars, cellular phones, palmtops, etc) mobile users (locate users independently of the device
they are currently using) mobile software (e.g., mobile agents)
How to find their location - Two extremes Search everywhere Store their current location everywhere Searching vs. Informing
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Locating Moving Objects
What (granularity), where (availability) and when (currency) to store
Ava
ilab
ilit
y
nowhere
at all sites
At selective sites (e.g., at frequent callers)
CurrencyNever update
Always update (at each movement)
Granularity
Exact location
the whole network
some partition
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Architectures of Location DBs
Two-tier Schemes (similar to cellular phones) Home Location Register (HLR): store the location of
each moving object at a pre-specified location for the object
Visitor Location Register (VLR): also store the location of each moving object mo at a register at the current region
Hierarchical Schemes Maintain multiple registries
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Two-tier Location DBs
Search Check the VLR at your current location If object not in, contact the object’s HLR
Update Update the old and new VLR Update the HLR
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Hierarchical Location DBs
Maintain a hierarchy of location registers (databases)
A location database at a higher level contains location information for all objects below it
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Hierarchical Location DBs
Call
caller
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Hierarchical Location DBs
Move
old locationnew location
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Hierarchical vs. Two-tier
(+) No pre-assigned HLR
(+) Support Locality
(-) Increased number of operations (database operations and communication messages)
(-) Increased load and storage requirements at the higher-levels
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Locating Moving Objects
Partitions
P1 P2
P3
P4 P5
User x User x
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Locating Moving Objects
Caching cache the callee’s location at the caller
(large Call to Mobility Ratio)
Replication replicate the location of a moving object at its frequent callers (large
CMR)
Forwarding Pointers do not update the VLR and the HLR, leave a forwarding pointer from
the old to the new VLR (small CMR) When and how forwarding pointers are purged?
Concurrency, coherency and recovery/checkpointing of location DBs
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Querying Moving Objects
• Besides locating moving objects, answer more advanced queries, e.g.,
• find the nearest service
• send a message to all mobile objects in a specific geographical reafion
• Location queries: spatial, temporal or continuous
•Issues: representation, evaluation and imprecision
Most current research assumes a centralized location database
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Querying Moving Objects
How to model the location of moving objects?
Dynamic attribute (its value change with time without an explicit update) [e.g., in MOST]
For example, dynamic attribute A with three sub-attributes: A.value, A.updatetime and A.function
(function of a single variable t that has value 0 at time t=0)
• The value of A at A.updatetime is A.value
• at time A.updatetime + t0 is A.value + A.function(t0)
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Querying Moving Objects
How to represent and index moving objects?
Spatial indexes do not work well with dynamically changing values
Value-time representation
• An object is mapped to a trajectory [Kollios 99]
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Outline
Motivating Example Issues: Mobility, Wireless Communication, Portability Adaptability and Mobile Client-Server Models Location Management Broadcast data dissemination Disconnected database operations Mobile Access to the Web Mobility in Workflow Systems State of Mobile DB Industry and Research Projects Unsolved Problems
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Information
Dissemination
Goal : Maximize query capacity of servers, minimize energy per query at the client.
Focus: Read-only transactions (queries).– Clients send update data to server – Server resolves update conflicts, commits updates
1. Pull: PDAs demand, servers respond. backchannel (uplink) is used to request data and provide
feedback. poor match for asymmetric communication.
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Information Dissemination…
2. Push: Network servers broadcast data, PDA's listen. PDA energy saved by needing receive mode only. scales to any number of clients. data are selected based on profiles and registration in each
cell.
ServerClients
A B CD
G F E
. .
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Information Dissemination…
ServerClients
A B CD
G F E
. . 14.4 Kbps
3. Combinations Push and Pull (Sharing the channel). Selective Broadcast: Servers broadcast "hot" information only.
"publication group" and "on-demand" group. On-demand Broadcast: Servers choose the next item based on
requests. FCFS or page with maximum # of pending requests.
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04/18/23 22:12 50
Broadcast Data Dissemination
business data, e.g., Vitria, Tibco election coverage data stock related data traffic information sportscasts, e.g., Praja
Datatacycle [Herman] Broadcast disks
Data Server
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Organization of Broadcast data
Flat: broadcast the union of the requested data cyclic.
Skewed (Random): broadcast different items with different frequencies. goal is that the inter-arrival time between two
instances of the same item matches the clients' needs.
A B C
A A B C
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Broadcast Disks
Multi-Disks Organization [Acharya et. al, SIGMOD95]
The frequency of broadcasting each item depends on its access probability.
Data broadcast with the same frequency are viewed as belonging to the same disk.
Multiple disks of different sizes and speeds are superimposed on the broadcast medium.
No variant in the inter-arrival time of each item.
B C
ADisk1
Disk2A B A C
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04/18/23 22:12 53
Selective
Tuning
Basic broadcast access is sequential Want to minimize client's access time and tuning time.
active mode power is 250mW, in doze mode 50μW What about using database access methods? Hashing: broadcast hashing parameters h(K) Indexing: index needs to be broadcast too
"self-addressable cache on the air"
(+) "listening/tuning time" decreases
(-) "access time" increases
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04/18/23 22:12 54
Access Protocols
Two important factors affect access time:
1. Size of the broadcast
2. Directory miss factor - you tune in before your data but after your directory to the data!
Trade-Off: Size means Miss factor
Trade-Off: Size means Miss factor
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Indexing
(1,M) Indexing: We broadcast the index M times during one version of the data.
All buckets have the offset to the beginning of the next index segment.
Distributed Indexing Cuts down on the replication of index material Divides the index into:
– replicated top L levels, non-replicated bottom 4-L levels
Flexible Indexing Broadcast divided into p data segments with sorted data. A binary control index is used to determine the data segment A local index to locate the specific item within the segment
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04/18/23 22:12 56
Caching in
Broadcasting
Data are cache to improve access time Lessen the dependency on the server's choice of broadcast priority Traditionally, clients cache their "hottest" data to improve hit ratio Cache data based on PIX: Probability of access (P)/Broadcast frequency (X). Cost-based data replacement is not practical:
requires perfect knowledge of access probabilities comparison of PIX values with all resident pages
Alternative: LIX, LRU with broadcast frequency pages are placed on lists based on their frequency (X) lists are ordered based on L, the running avg. of interaccess
times page with lowest LIX = L/X is replaced
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Prefetching in Broadcasting
Client prefetch page in anticipation of future accesses No additional load to the server and network Prefetching instead of waiting for page miss can reduce the cost of a
miss PT prefetching heuristic [Archarya et al. 96]
- pt: Access Probability (P) * period (T) before page appears next- A broadcast page b replaces the cached page c with lowest pt
value Team tag - Teletext approach [Ammar 87]
Each page is associated with a set of pages most likely to be requested next
When p is requested, D (D:cache size) associated pages are prefetched
Prefetching stops when client submit a new request
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Cache
Invalidation Techniques
When? Synchronous: send invalidation reports periodically Asynchronous: send invalidation information for an item as
soon as its value changes; E.g., Bit Sequences [Jing 95] To whom?
Stateful server: to affected clients Stateless server: broadcast to everyone
What? invalidation: only which items were updated propagation: the values of updated items are sent aggregated information/ materialized views
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Synchronous
Invalidation
Stateless servers are assumed. Types of client: Workalcholic and sleepers [Barbara Sigmod 94] Strategies:
Amnestic Terminals: broadcast only the identifiers of the items that changed since the last invalidation report
abort T, if x є RS(T) appears in the invalidation report Timestamp Strategy: broadcast the timestamps of the latest
updates for items that have occurred in the last w seconds.
abort T, if ts(x) > tso(T) Signature Strategy: broadcast signatures.
A signature is a compressed checksum similar to the one used for file comparison.
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Consistency and
Currency
Only committed data are included in the broadcast Does a client read current and consistent data? Currency interval is the fraction of bcycle that
updates are reflected Span(T) is the # of currency intervals from which T
read data if Span(T) = 1, the T is correct (read consistent data)
else ?
... several consistency models
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Consistency Criteria
Latest value: clients read the most recent value of a data item [Garcia-Molina TODS82, Acharya VLDB96]
Serializability: Certification reports [Barbara ICDCS97] Update consistency: clients commit of their reads are not
invalidated – read mutually consistent data F-Matrix method [Shanmugasundaram SIGMOD99]
2-level serializability: Each client is serializable with respect to the server SGT method [Pitoura&Chrysanthis ICDS99] Multiversion [Pitoura&Chrysanthis VLDB99]
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04/18/23 22:12 62 VLDB 1999
10
begin (first read) first invalidation commit
Multiversioningwith invalidation
InvalidationVersioningMultiversioning
T’s lifetime
Currency in Multiversion Schemes
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Adaptive Hybrid Broadcast
Cycle-based, bidirectional hybrid broadcast server Issues:
Dynamic computation of bandwidth allocated to each broadcast mode
Dynamic classification of data items (periodic vs. on-demand)
Scheduling periodic and on-demand broadcasts
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An Approach
After each broadcast cycle, items classified as periodic or on-demand, depending on bandwidth savings expected
Periodic broadcast occupies up to BWThreshold Periodic broadcast program is computed to satisfy all
deadlines of periodic data On-demand broadcast uses on-line EDF
(Earliest Deadline First) algorithm + batching
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Outline
Motivating Example Issues: Mobility, Wireless Communication, Portability Adaptability and Mobile Client-Server Models Location Management Broadcast data dissemination Disconnected database operations Mobile Access to the Web Mobility in Workflow Systems State of Mobile DB Industry and Research Projects Unsolved Problems
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Disconnected Operations
Issues: Cache misses are more expensive in mobile environments. Data availability for disconnected operation Data consistency given that global communication is costly Autonomy vs. Consistency
Solutions: Caching Prefetching Hoarding Eventual consistency
– Assumption: simultaneous sharing other than read is rare. Update conflict detection/resolution
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Caching
What to cache? Entire files, directories, tables, objects Portions of files, directories, tables, objects
When to cache? Is simple LRU sufficient? LRU captures an aspect of temporal locality Predictive/semantic caching: based on the
semantics distance between data/request
E.g., clustering of queries [Ren 99]
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Prefetching
Online strategy to improve performance prepaging prefetching of file prefetching of database objects
What to fetch? access tree (semantic structure) probabilistic modeling of user behavior
Old idea that can be used during network idle times Combine delayed writeback and prefetch
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Hoarding
Planned and Accidental disconnections are not considered failures.
New idea - Hoarding:a technique to reduce the cost of cache misses during
disconnection.
That is, load before disconnect and be ready. How to do hoarding?
user-provided information (client-initiated disconnection)– explicitly specify which data– Implicitly based on the specified application
access structured-based (use past history)
E.g., tree-based in file systems, access paths (joins) in DBs
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Hoarding in DB Systems
Granularity of Hoarding RDBMS: ranges from tables, set of tables, whole relations OO & OR DBMS: objects, set of objects or class
Hoard by issuing queries or materialized views User may explicit issue hoarding queries
E.g., Create View with Update-On clause [Lauzac 98]
OO query to describe hoarding profiles [Gruber 94] History of past references both queries and data objects Hoard Keys - an extended database organization [Badrinath 98]
– hoard keys are used to partition a relation in disjoint logical horizontal fragments
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Processing the Log
What information to keep in the log for effective reintegration and log optimization? Data values, timestamps, operations
Goal: Keep the log size small to Save memory Reduce cost for update propagation and reintegration
When to optimize the log Incrementally each time a new operation is added Before propagation or integration
Optimizations are system specific E.g., keep last write record, drop records of inverted operations
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Cache Coherence/Data Consistency
"Lazy" or weak consistency promises high availability Consider some conflicts (e.g., write-write conflicts) Read-any/Write-any
Other schemes are costly: Pessimistic replication schemes/Quorum schemes Server-initiated callbacks for cache invalidation
(e.g., Leases). Optimistic replication schemes
System support for detection of conflicts: version vector, timestamps automatic resolution or manual resolution (tools)
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Techniques to Increase Autonomy
Mobile Database Consistency When a mobile database M shares a data item with another
database D, it is involved in a global integrity constraint C. Transactions on both M and D may suffer unbounded and
unpredictable delays - No local commitment. What about localizing the constraints – no global constraints?
Localization:reformulates C so that M accepts a local constraint C’ instead Local transactions remain local. When C’ is violated at a node, it requests the others for re-
localization, i.e., a dynamic readjustment of C’. – No need for a distributed transaction.– No inconsistency from concurrent requests
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Localization of Constraints
Simple Example: Let x and y be two data items at two nodes. C = J.x + K.y > D is a global constraint. Localization yields two local constraints:
x > L1 and y > L2 where L1 and L2 are constants chosen such
that J.L1 + K.L2 > D Re-localization: L1, L2 can be changed: node y
increases L2 before node x decreases L1
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Localization Methods
Escrowing: Logically partitions aggregated items Escrow transactions [O’Neil 86] Demarkation protocol [Barbara 91]
Geormetric Method [Mazumdar 99]: Enhanced Escrowing It tackles quadratic inequalities
Fragmentation [Walborn 95]: Physically partitions item with constraints localized within the individual fragments Fragmentable objects: fragments are merged to the
originating position Reorderable Objects: fragments can be re-organized during
the merging
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Two-tier Transaction Models
Tentatively Committed Transactions Transactions tentatively commit on a mobile unit Make their results locally visible leading to abort dependencies Certification based on application or system defined criteria invalidated trans. are aborted, reconcile, or compensated
Isolation-Only Transactions [Lu 94] First-class transactions for connected operations
– immediately commit at the server, globally serializable Second-class transactions for disconnected operations
– tentatively commit, locally serializable, no failure atomicity– validation criteria: global serializability, global certifiability– invalidated trans. are aborted, reexecuted, or compensated.
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Two-tier transaction Models
Two-tier Replication [Gray 95] Base transactions and Tentative transactions (disconnected) Upon reconnection, tentative transactions are reprocessed
as base transactions on master data version Application semantics are used to increase concurrency and
acceptance (e.g., commutative operations)
(Mobile) Escrow Transactions Transactions are validated locally by localizing constraints Local commitment ensures global commitment
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Mobile Transactions
Distributed transactions involving both mobile and fixed hosts. Traditional approaches are too restrictive.
Mobile Open Nested Transactions [Chrysanthis 93]
Goals: sharing of partial results while in execution,
maintaining computation state on a fixed host,
moving transactions on/off a mobile host and across fixed hosts. Components: Atomic transactions, Compensatable transaction,
Reporting transactions and Co-transactions. Properties: Component isolation, semantic atomicity
Components may commit/abort independently
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Mobile Transactions
Kangaroo Transactions [Dunham 97] Transaction relocation is achieved by splitting the transaction
during hand-off. One Joey transaction per cell.
The Clustering Model [Pitoura 95] A distributed database is divided into weak and strict clusters Data in a cluster are mutually consistent Inconsistency between clusters is bounded and resolved by
merging them either – during transaction commitments, or – when connectivity improves
A mobile transaction is decomposed into Strict and Weak transactions based on consistency requirements
Only strict transactions ensure durability and currency of reads
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Failure Recovery
Emphasis has been on recording global checkpoints Periodically store the state of a distributed application with
mobile components. DB Failure Recovery: Logging and checkpointing Failures can be soft or hard
Soft failure can be recovered from the locally stored log and checkpoint
Hard failure require hard checkpoints stored in the fixed network.
Issues: When to propagate the log and create a hard checkpoint? Where to store hard checkpoints to speed up recovery and
reduce its cost?
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Database Interface
Desirable features: Semantic simplicity: formulation of queries without
special knowledge Interaction with a pointing device Disconnected query specification
QBI (Query By Icons) [Massari-Chrysanthis 95] Iconic language requiring minimum typing Semantic data model that hides details Metaquery tools for query formulation during
disconnections
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Outline
Motivating Example Issues: Mobility, Wireless Communication, Portability Adaptability and Mobile Client-Server Models Location Management Broadcast data dissemination Disconnected database operations Mobile Access to the Web Mobility in Workflow Systems State of Mobile DB Industry and Research Projects Unsolved Problems
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04/18/23 22:12 84
Mobile Access to the Web
Three-tier Architectures: Client - Web Server - Data Server Web Server can act like a server-side agent
Prefetching at its cache can hide some latency Scripts at the Web server can perform user-specified
filtering and processing. Most solutions use a Web proxy to avoid any changes to the
browsers and servers. Pythia [Fox96] Mobile Browser (MOWSER) [Joshi 96]
– Distillation: highly lossy, real-time,datatype specific compression that preserves semantic content
WebExpress [Housel 97]
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WebExpress
Utilizes the C-I-S Model Goals: reduce traffic volume and reduce latency Intercept any http request and perform four optimizations:
Caching at both CSA & SSA of graphics and html objects
Differencing: only changes are communicated Long-live TCP/IP Connection: CSA & SSA use a single
TCP connection Header reduction: SSA includes the required browser
capabilities. They are not sent by the CSA. While disconnected (off-line mode) uses CSA cache
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04/18/23 22:12 86
Advances in Mobile Web Servers
W4 for Wireless WWW [bartlett 94]: Mosaic on PDA
Dynamic Documents: Tcl scripts that execute within the mobile browser to customize the html documents
Dynamic URLs [Mobisaic 94]: They support mobile web servers and work with active pages.
IPiC [Shrinivasan 99]: A match head sized web server
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Mobility in Workflows
Workflows are automated business processes.
involve coordinated execution of multiple long-running tasks or activities
Workflow system coordinates the workflow execution.
Processing entities (clients) are where the activities are executed and can be mobile.
• disconnections among procesing entities (clients)
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Workflow Disconnected Operations
A pessimistic approach: Exotica
Prior to disconnection, each client:
reserves the activities it plans to work by locking
hoards the relative to the activities data (requests from the server the input containers of the activities)
During disconnection,
stores results in its local stable memory
Upon reconnection,
the results are reported back to the server
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Mobile Agents in Workflows
A Mobile Agent Workflow Model: INCAS
No centralized workflow server
Each workflow process is model as a mobile agent called Information Carrier (INCA). Each INCA
encapsulates the private data of the workflow
carries a set of rules that control the flow between the activities of the INCA computation
maintains the history (log) of its execution
Each INCA is initially submitted to a procesisng entity (client) and roams among processing entities to achieve its goal
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Outline
Motivating Example Issues: Mobility, Wireless Communication, Portability Adaptability and Mobile Client-Server Models Location Management Broadcast data dissemination Disconnected database operations Mobile Access to the Web Mobility in Workflow Systems State of Mobile DB Industry and Research Projects Unsolved Problems
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Mobility Middleware in the Market
Most middleware market are based on TCP/IP and socket-oriented connections
Wireless-friendly TCP versions have been proposed but no major products adopted it
Microsoft’s Remote Access supports cellular communication by integrating Shiva’s PPP suite
Shiva’s PPP (Point-to-Point protocol) suit provide a remote access client to either wired or mobile servers E.g., mobile clients can access Tuxedo transaction services
MobileWare Office Server: An agent-based middleware that supports Lotus Notes, Web access, database replication, etc. Connection profiles, checkpointing,compression, security
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State of Mobile DB Industry
Sybase SQL Remote (Sybase SQL AnyWhere) MobiLink: Centralized model to control replication Application-specific bi-directional synchronization using scripts UltraLite: in-memory dbms (50KB)
ORACLE Oracle Mobile Agents middleware Oracle 8 Lite: supports bi-directional replication between a client
and a server (50-750KB) Oracle Replication Manager: supports replication across
multiple servers based on the peer-to-peer model MS SQLServer
Merge replication and conflict resolution Alternative clients: Outlook and MS ACCESS
IBM DB2 Everywhere (100KB)
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Case Study: Coda
Client-Server System with two classes of replication w.r.t. consistency
Disconnected vs. Weakly connected Hoarding, Caching/Server callback, No Prefetching
During connections: Allows AFS clients (Venus) to hoard files. hierarchical, prioritized cache management equilibrium. track dependencies, bookmarks
During disconnections: Venus acts as (emulates) a server generates (temp) fids, services request to hoarded files.
On reconnection, Venus integrates locally changed files to servers. Considers only write-write conflicts - no notion of atomicity User conflict resolution/ Application-aware adaptation [Odyssey] Use optimistic replication technique
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Case Study: Consistency in Bayou
A bottom-up approach to specific design problems more distributed than coda, more emphasis on "small" clients
Key features: read-any/write-any to enhance availability anti-entropy protocol for eventual consistency dependency checks on each write
– dependency set– If queries (run at server) do return the expected results– Application-specific resolution of update conflicts
Primary server to commit writes and set order Session consistency guarantees
How effective is anti-entropy?
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Anti-entropy Protocol
Server propagates write among copies. Eventual all copies "converge" towards the same state. Eventual reach identical state if no new updates. Pair-to-peer anti-entropy
each server periodically selects another server exchange writes and agree on the performed order reach identical state after performing the same writes
in the same order.
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Case Study: Rover
Rover [Joseph 97] provides an environment for the development of mobile applications
Applications are split into client and server part communicating with Queued RPCs
Application code and data are encapsulated within Relocatable Dynamic Objects (RDOs)
Access Managers at client and server handle RDOs Client’s operational log is lazily transfer to the server Disconnections are supported by the local cache Some support for primary copy, optimistic consistency
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Case Study: Pro-Motion
Pro-Motion [Chrysanthis 97] is designed for the development of mobile database applications.
It shares similar architecture as Rover with a multi-tier C-I-S model. Compact is the unit of caching and hoarding
It encapsulates cached data, methods, consistency rules and obligations (e.g., deadlines).
Supports both tentatively committed transactions
and two-tier replication.
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Case Study: Rome
Rome [Fox 99] goals is the timely and in context delivery of information
Information should be received when and where it is needed Its fundamental building block are the triggers:
pieces of data bundled with contextual information Condition: (location R) (time t) action
It is similar to active databases but with decentralized management
It provides an extensible framework and building blocks leveraging on internet service.
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Unsolved Problems
Integration and evaluation of algorithms with applications Broadcast disks
Information update/consistency and data temporal coherence - meet time constraints of requests
Relation between server broadcasting and client caching. Multiple broadcast channels and multiple database access Efficient, scalable, adaptive mechanisms
Location handling Trigger management
Programmer Interface for Application-aware adaptation Data fidelity vs. consistency Semantic consistency needs metadata/requirements
Multimedia and QoS
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To Recap
Mobile and wireless computing attempts to deliver today’s and tomorrow’s applications on yesterday’s hardware and communication infrastructure!