Mobile and Wireless Database Access for Pervasive Computing

Mobile and Wireless Database Access for Pervasive Computing
An IEEE ICDE 2000 Tutorial on Mobile and Wireless Database Access for Pervasive Computing Panos K. Chrysanthis University of Pittsburgh & Carnegie Mellon University Evaggelia Pitoura University of Ioannina

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

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.

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.

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.

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…

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.

Wireless Communication
Cellular - GSM (Europe+), TDMA & CDMA (US) FM: Kbps; Digital: 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 ) 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)

Mobile Network Architecture

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

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) Li-Ion (Lithium-Ion)

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

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 Query By Icons (QBI): Iconic visual Language [Massari&Chrysanthis95]

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 bursty network activity during connections

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

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 prioritization of actions upon reconnection PDAs are unreliable

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 Caching: what, when and how charging the laptop with "local" info

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)

Adaptability Where should support for mobility and adaptability be placed? Application-Aware Laissez-Faire Application Transparent (-) 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 (+) existing applications continue to work unchanged (-) too general, cannot take advantage application semantics (-) may not be attainable (e.g., during a long disconnection)

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.

C-SA-C: Server-side Agent
Client Server Agent Fixed Network Wireless Link 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

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

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 Various optimizations: messaging and queying manipulate data prior to their transmission to the client: e.g., perform data specific compression, batch together requests, change the transmission order

C-CA-S: Client-side Agent
Mobile Host Client Server Agent Fixed Network Wireless Link C-SA-S: The Client/Client-side Agent/Server Model caching background prefetching and hoarding various communication optimizations

C-I-S: Client & Server Agents
Wireless Link Fixed Network Client Agent Agent Server Mobile Host 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

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]

A Taxonomy

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

What (granularity), where (availability) and when (currency) to store at all sites Availability At selective sites (e.g., at frequent callers) the whole network Exact location nowhere some partition Currency Granularity Never update Always update (at each movement)

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

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

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

caller

Move new location old location

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

Partitions P3 P1 P4 P5 P2 User x User x

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

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 When and how often continue queries be re-evaluated, Imprecission Most current research assumes a centralized location database

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) Answer future queries tentatively.

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]

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.

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. Server Clients A B C D G F E BC EG AB . .

Information Dissemination…
Server Clients A B C D G F E BC EG AB . . 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.

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

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

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 A Disk1 Disk2 A B A C

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 Access time: avg time elapsed between the beginning of the search for an item to the reading of it from the broadcast channel Listening/Tuning time: the amount of time spent listening to the broadcast channel

Access Protocols Two important factors affect access time:
Size of the broadcast 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

Indexing (1,M) Indexing: Distributed Indexing Flexible 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 After broadcasting 1/M of the file we broadcast the index.

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

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

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 Synchronous invalidation adds some latency in query processing

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. Synchronous methods surpass asynchronous ones because allow clients To periodically tune in. But if inactive longer than the broadcast period? Validate/reload the cache. (volume checking in CODA group identifier rather than item)

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

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]

Currency in Multiversion Schemes
Versioning Multiversioning Multiversioning with invalidation Invalidation begin (first read) first invalidation commit T’s lifetime 10 VLDB 1999

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

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

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

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]

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

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

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

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

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)

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 Local transactions remain local; the problem of unbounded delays is mitigated.

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

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 Escrowing: applies on indistinguishable units. Operations: Inc and Dec.

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.

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

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 The components of a mobile transaction executes on both mobile and fixed hosts Reporting can be compensatable or non-compensatable

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

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?

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

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]

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 Web server is stateless. The HTTP protocol requires transmission of browser capabilities

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

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)

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

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 History (log) merging

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 Major middleware players are Novell’s Netware and MS Remote Access Popular transport layer…

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)

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

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?

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.

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

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.

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.

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 Adaptation for changes in available resources, resource management with dynamic data replication.

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

Mobile and Wireless Database Access for Pervasive Computing

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