6/30/2015 01:07 1 mobile and wireless database access for pervasive computing panos k. chrysanthis...

04/18/23 22:12 1

Mobile and Wireless Database Access for Pervasive Computing

Panos K. ChrysanthisUniversity of Pittsburgh & Carnegie Mellon University

Evaggelia Pitoura University of Ioannina

panos@cs.pitt.edu pitoura@cs.uoi.gr

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

FIXEDHOSTBASE

STATION

BASESTATION

Mbps to Gbps

MOBILE HOST

WIRELESS LAN CELL2Kbps - 15Mbps

WIRELESS RADIO CELL9Kbps - 14Kbps

BASESTATION

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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

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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

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-CA-MS

C-SA-MS

C-I-MS

WirelessWeb Browser

Pro-motionRover

WebExpress

Pro-motionOracle mobile

agents

Odyssey

Adaptability

Strategy

Laissez-Faire ApplicationMobilityAware

ApplicationTransparent

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Outline

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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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What (granularity), where (availability) and when (currency) to store

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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caller

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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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Partitions

User x User x

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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

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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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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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

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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

ServerClients

A B CD

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Information Dissemination…

ServerClients

A B CD

. . 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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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 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.

ADisk1

Disk2A B A C

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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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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

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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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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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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]

04/18/23 22:12 62 VLDB 1999

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

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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

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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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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

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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

04/18/23 22:12 92

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)

04/18/23 22:12 93

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

04/18/23 22:12 95

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?

04/18/23 22:12 96

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.

04/18/23 22:12 97

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

04/18/23 22:12 98

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.

04/18/23 22:12 99

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.

04/18/23 22:12 100

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

04/18/23 22:12 101

To Recap

Mobile and wireless computing attempts to deliver today’s and tomorrow’s applications on yesterday’s hardware and communication infrastructure!

6/30/2015 01:07 1 mobile and wireless database access for pervasive computing panos k. chrysanthis...

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