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Managing Dynamic Metadata and Context

This research delves into managing dynamic metadata and context in service collaborations, focusing on Gaggle of Services, Multimedia Collaboration, GIS/Sensor Grids, and Metadata Systems. It addresses practical problems, requirements, and different metadata systems, discussing challenges and proposed solutions.

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Managing Dynamic Metadata and Context

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  1. Managing Dynamic Metadata and Context Mehmet S. Aktas Computer Science, Informatics, Pervasive Technology Laboratories Indiana University Bloomington IN 47401 maktas@cs.indiana.edu

  2. Outline • Motivation • Research Issues • Proposed Approach • Evaluation • Conclusions • Future Work

  3. Context as Service Metadata in Gaggle of Services • Context is metadata associated to both services and their activities • interaction-independent • slowly varying, quasi-static context • Ex: type or endpoint of a service, less likely to change • interaction-dependent, generated as result of interaction of services • dynamic, highly updated context • information associated to a single service, a session (service activity) or both • Ex: session-id, URI of the coordinator of a workflow session • Gaggle of Services • set of actively collaborating managed services dynamically assembled for specific tasks • generate events as result of interactions • very small part of the whole Grid

  4. Collaboration Grids • Multimedia Collaboration domain • collaborative A/V sessions with varying types of dynamic metadata describing group of participants • real-time metadata describing audio/video streams • Collaboration Grids has also static metadata • information about service, available sessions, and media servers • needs a distributed real-time session metadata management systems • Characteristics of the domain • widely distributed services • metadata of events (archival data) • mostly read-only • persistent, but lifetime is bounded to lifetime of events • QoS metadata associated to A/V services, media server, etc…

  5. GIS/Sensor Grids • Workflow-style applications in Geographic Information System and Sensor Grids • sensor grid data services generates events when a certain magnitude event occurs • firing off various codes, filtering, analyzing raw data, generating images, maps • needs a distributed workflow session metadata management systems to correlate workflow activities • Characteristics of domain • any number of widely distributed services can be involved • conversation metadata • transient • multiple writers • rarely changing descriptive, prescriptive service metadata

  6. Problem Space and Requirements • Practical Problem: We need management of all information associated with services in Gaggle of Services for; • correlating activities of widely distributed services (1, 2) • enabling uniform query capabilities to both dialog or monolog context information (3, 4) • “Give me list of services satisfying C:{a,b,c..} QoS requirements and participating S:{x,y,z..} sessions” • management of events especially in multimedia collaboration • providing information to enable (5) • real-time replay/playback and • session failure recovery capabilities • Requirements • dynamism • performance • uniformity • interoperability • persistence

  7. Different Metadata Systems- I • There are different standards defining interaction-independentmeta-data, such as UDDI and its extentions • And many different implementations from (extended) UDDI through MCAT of the Storage Research Broker • And of course representations including RDF and OWL • Further there is system metadata (such as UDDI for core services) and metadata catalogs for each application domain such as WRS (Web Registry Service) for GIS • They have different scope and different QoS trade-offs • e.g. Distributed Hash Tables (Chord) to achieve scalability in large scale networks • UDDI-Extensions

  8. Different Metadata Systems- II • There are various technologies addressing interaction-dependentmeta-data. • Point-to-Point • WS-Metadata Exchange • WS-Resource Framework • Point-to-Point methodologies • are limited to communication with metadata only from the two services. • do not scale in managing activities of widely distributed services in workflow style grid applications • WS-Context is promising it has limitations • limited query capability • lack of support interaction-independent metadata • centralized – single point of failure, performance bottleneck • Centralized • WS-Context • Centralized • WS-Context

  9. Motivations • Lack of support for providing uniform programming interface (with advanced query capabilities) to • large scale relatively static metadata as in searchable repository of all the world’s services and session related dynamic metadata • Lack of support for managing small scale highly dynamic metadata as in dynamic workflows for sensor integration and collaboration • fault-tolerance andability to support dynamic changes with few millisecond delay • but only a modest number of involved services (up to 1000’s in a session) • ability to adapt instantaneous changes in client demands • need Session NOT Service/Resource meta-data

  10. Research Issues • How can we achieve a standard wayof publishing inquiring both interaction-independent and conversation-based service metadata througha uniform programming interface? • What is a novel architecturefor a decentralized Information Service managing dynamic session-related metadataof widely distributed services? • For building a decentralized metadata-system, we investigate research issues related with; • performance • scalability • fault-tolerance • consistency enforcement

  11. Our approach: Hybrid WS-Context XML Metadata Service • We designed and built a WS-Context compliant XML Metadata services supporting distributed or central paradigms. This service aFault Tolerant and High Performance Information Service (FTHPIS). • supports extensive metadata requirements of rich interacting systems, such as • correlating activities of widely distributed services, EX: workflow style GIS Service Oriented Architectures, AND • optimizing Grid/Web Service messaging performance, EX: mobile computing environment, AND • managing dynamic events especially in multimedia collaboration, EX: collaboration Grid/Web service applications, AND • providing information to enable session failure recovery capabilities.

  12. Hybrid XML Metadata Service WS-Context + UDDI • We combineextended functionalities of these two services: WS-Context AND UDDI in one hybrid service to manage Context (service metadata). • extended WS-Context controlling a workflow • extended UDDI providing a searchable repository for services • This approach meets the interoperability and uniformity requirements of the problem. • Our approach enables advanced query capabilities on service metadata • hybrid functions operating on both metadata spaces • extended WS-Context functions operating on session metadata, (parent-child relationships are implemented) • extended UDDI functions operating on interaction-independent metadata • information security functions providing a simple authentication and authorization mechanism to the shared data.

  13. FTHPIS Client FTHPIS Client WSDL WSDL HTTP(S) WSDL WSDL Hybrid WSContext Service WSDL JDBC WSDL Database Extended UDDI Service JDBC Database Hybrid WS-Context XML Metadata Service Hybrid WSContext Service interface combining Extended UDDI and WS-Context WSDL Descriptions uddi_wscontext.wsdl Extended UDDI WSDL Service Interface Descriptions uddi_extended.wsdl HTTP

  14. Extended UDDI XML Metadata Services • We also designed and implemented an extended UDDI XML Metadata Service (alternative to OGC Web Registry Services). This service, • supports GIS Metadata Catalog (functional metadata), user-defined metadata ((name, value) pairs), up-to-date service information (leasing), dynamic aggregation of geospatial services. • Our approach enables advanced query capabilities • geo-spatial and temporal queries , • metadata oriented queries, • domain independent queries such as XPATH queries on metadata catalog.

  15. Key Design Features • Message Dissemination • communication method among the nodes of the network • Caching • usage of memory-built-in storage running on each node to minimize latency and meet the performance requirement • Access • methodology for redirecting client request to an appropriate replica server to meet dynamism and the performance requirements • Storage • methodology for replicating data to meet fault tolerance and performance requirements • Consistency enforcement • methodology to ensure all replicas of a context to be the same

  16. Message Dissemination • Publish-Subscribe exploited to support replicated storage e.g. • Initial storage of context • Dissemination of context access requests • Dissemination of updates to make copies consistent • We used open source NaradaBrokering software to provide multi-publisher multicast communication mechanism • topic based publish/subscribe messaging system • runs on a network of cooperating broker nodes. • provides support for variety of QoSs, such as low latency, reliable message delivery, multiple transfer protocols, security, and so forth.

  17. Client Client WSDL WSDL WSDL WSDL Hybrid-WSContext Service WSDL JDBC Database Topic Based Publish-Subscribe Messaging System WSDL Extended UDDI Service WSDL WSDL JDBC Hybrid-WSContext Service Hybrid-WSContext Service Database JDBC JDBC Database Database Distributed Hybrid WS-Context XML Metadata Services HTTP(S) HTTP Subscriber Publisher Replica Server-1 Replica Server-2 Replica Server-N

  18. Caching Strategy • TupleSpaces paradigm exploited to support caching • asynchronous communication • pioneered by David Gelernter • communication units are tuples • data-structure consisting of one or more typed fields • Hybrid WS-Context Service employs/extends TupleSpaces: • use of A light-weight implementation of JavaSpaces • all memory accesses. overhead is negligible (less than 1msec. for inquiries) • data sharing - mutual exclusive access to tuples • associative lookup - content based search, appropriate for key-based caching • temporal, spatial uncoupling of communicating parties • e.g. a tuple: ("context_id", Context). This indicates a tuple with two fields: a) a string, "context_id" and b) a Java object, "Context". • back-up with frequent time intervals for fault-tolerance

  19. Access: Request Distribution • Peer-to-Peer based message distribution methodology exploited for redirecting a client request to the appropriate replica server • Use of pub-sub system for request distribution • broadcast-based Context access request dissemination • servers that can satisfy the query unicast a response with a copy of the context under demand • Advantages: does not keep track of locations of every single data, makes use of redundant copies kept only for fault-tolerance reasons, improves the responsiveness • Practical Problem: If the number of repetitive queries that require probing the network increased, this may amplify the network consumption and affect the system performance • Approach: use of dynamic replication for moving/replicating highly-demanded copies in the proximity of their requestors to minimize the need for probing the network

  20. Storage: Replica placement • Peer-to-Peer based message distribution methodology exploited for creating initial permanent-copies of a context • Use of pub-sub system for permanent-replication • Use of non-blocking replica placement • 1st step: initiator creates a temporary copy at every capable replica server • 2nd step: initiator keeps permanent copies only at a few first answering replica servers for fault-tolerance • Advantages:[1] the publishing client does not block until the replication is completed, [2] a temporary full-replication methodology exploited to improve the responsiveness, [3] permanent-copies remain as backup facility to meet the fault-tolerance requirement

  21. Storage: Dynamic replication • Dynamic replication methodology exploited for creating server-initiated (temporary) copies of a context • Use of pub-sub system for server-initiated replication • replication decision belongs to the server (autonomous) • we keep the popularity (# of access requests) record for each copy of a context and flush it on regular time intervals • unpopular server-initiated copies of a context are deleted • popular copies of a context are moved in the proximity of their requestors (where the requests are originated) • very popular copies of a context are replicated in the proximity of their requestors (where the requests are originated) • Advantages:[1] this strategy exploits locality which in turn improves the responsiveness, [2] this strategy also captures dynamism by adjusting the system to changing user demands

  22. Consistency enforcement • Consistency enforcement methodologies exploited to keep copies of a context consistent. • Use of weak consistency model: copies of a context can be different, however, updates are propagated to replicas whenever it is needed for consistent view of information. • Use of pub-sub system for update propagation • Use of primary-copy approach, all updates for a specific context are initiated at a single server • Use of synchronized timestamps (as versions) to give sequence to each published context to impose an order for concurrent write operations on the same data • updates are pulled by a replica server from the primary-copy if the replica server realizes that it has a stale copy • updates are pushed (broadcasted) by the primary-copy if it realizes that there exist a server that has not yet been updated

  23. Consistency enforcement - II • Advantage: this strategy employs non-blocking primary-copy approach, thus the publisher does not block until an update operation is completed that in turn improves responsiveness • Practical Problems: [1] with this strategy, one cannot update a data item more frequently than one operation per 30 milliseconds, which the NaradaBrokering NTP-protocol based synchronized timestamp accuracy. [2] with this strategy, a client cannot make sure if the update operation is carried out correctly. • Approach:1 update operation per 30 millisecond is acceptable update rate considering our application use domains. As the performance is a requirement, we favor solutions that do not require blocking client applications.

  24. Prototype Evaluation • We evaluated the prototype implementation for three distinct aspects of distributed systems: • Performance • baseline performance • effect of the network latency on the baseline performance • Scalability • performance degradation of the system under increasing message sizes or message rates • scalability gain both in numbers and in performance when moving from a centralized system to a distributed system under the same workload. • Fault-tolerance • the empirical cost of the fault-tolerance in terms of execution time of standard operations on a tight cluster or on a network with significant network distances

  25. Machine Configurations

  26. 1 user/1000 transactions Expeditor 1 user/1000 transactions Expeditor single threaded single threaded Publishing Querying Module Publishing Querying Module WSDL WSDL WSDL WSDL JDBC Handler JDBC Handler WS-Context Client Hybrid-WSContext Service WS-Context Client Hybrid-WSContext Service Test-2. Hybrid-WSContext inquiry/publication without database access Test -3. Hybrid-WSContext inquiry/publication with database access 1 user/1000 transactions 1 user/1000 transactions single threaded single threaded Extended UDDI Server Engine Dummy Server WSDL WSDL WSDL WSDL extended UDDI Client Extended UDDI Server Client Dummy Server Test-1. Dummy Server Test-4. extended UDDI inquiry/publication RESPONSIVENESS EXPERIMENT

  27. Test2 - Test1 is JavaSpaces overhead • If query can be satisfied by Javaspaces cache, the query can be satisfied in < 1ms plus the few milliseconds of Web service overhead • comparable performance for standard operations with the existing metadata management services.

  28. Expeditor Expeditor Publishing Querying Module Publishing Querying Module WSDL WSDL JDBC Handler JDBC Handler Hybrid-WSContext Service Hybrid FTHPIS-WSContext Service 5 Client distributed to cluster nodes 1 to 5, with each running 1 to 15 threads Thread Pool Thread Pool WSDL WSDL HTTP(S) SCALABILITY TEST-1 1 user/100 transactions single threaded WSDL WS-Context Client TEST-1 - Hybrid-WSContext inquiry/publication with increasing message sizes TEST-2 - Hybrid-WSContext inquiry/publication with increasing message rates (# of messages per second)

  29. Stdev=13.01 Stdev=10.07 Stdev=11.54 Stdev=6.72 Stdev=8.27 Stdev=6.95 Stdev=11.03 Stdev=3.09 Stdev=1.42 Stdev=2.68 • The results indicate that the system performs well for small-size context payloads. • The results also indicate that the cost of inquiry and publication operations remains the same, as the context’s payload size increases from 100Bytes up to 10KBytes.

  30. Stdev=33.52 Stdev=53 Stdev=39.49 Stdev=10.31 Stdev=0.91 Stdev=0.65 Stdev=0.97 • The system can scale up to 940 simultaneousquerying clients and 222 simultaneouspublishing clients where each client sending one query per second, for small size context payloads with 30 milliseconds backup interval time for fault tolerance. • Multi-core hosts will improve performance dramatically.

  31. 5 Client distributed to cluster nodes 1 to 5, with each running 1 to 15 threads firing messages to randomly selected servers. node-1 node-1 node-1 node-1 node-5 node-5 node-5 node-5 node-5 Thread Pool Thread Pool WSDL WSDL node-2 node-4 node-2 node-3 node-3 node-3 2 3 4 5 1 SCALABILITY TEST-2 HTTP(S) • We investigate scalability when moving from a centralized server to a distributed one under heavy workloads. • Numbered rectangle shapes correspond to an N-node FTHPIS system with various Publish-Subscribe topologies (this does NOT affect performance) • 5 different FTHPIS system tested when N range from 1 to 5 under the same workload. • At each testing case, same volume of data is evenly distributed among the nodes.

  32. Non-optimal caching algorithm as does database access BEFORE Publish-Subscribe. Reversingthis choice should lead to throughput Linear in #nodes Pub-Sub overhead ~ 2ms • The scalability of metadata store can be increased when moving from a centralized service to a distributed system.

  33. FAULT-TOLERANCE TEST

  34. FAULT-TOLERANCE TEST RESULTS • Fault-tolerance ?? vs. Performance??. • The lower the level of fault-tolerance, the higher the performance. • High degree of replication could be succeeded (by utilizing an asynchronous communication model) without increasing the cost of fault-tolerance.

  35. Summary of Contributions • specification on managing all service metadata • a method to achieve uniform programming interface to both interaction-independent and session-related metadata. This method also introduces a data model for storing session-related metadata • specification on managing interaction-independent service metadata • a method to achieve a Geographical Information Systems compatible, domain-independent and metadata-oriented management of interaction-independent service metadata • fault tolerant and high performance information service • a method to achieve management of dynamic metadata and Context in subgrids - dynamically assembled, collaborating, modest number of services - put together to perform a particular task

  36. Future work • transaction scheduling • Investigate how to minimize the time required to complete transactions on two diff. metadata systems with diff. time constraints • evaluation of dynamic replication • Carry out simulations for evaluation of dynamic replication • optimal caching methodologies • Implement and test more optimal caching methodologies • smoothening the impacts of backups on performance • Investigate how to minimize the impact of the time spent (high peeks) for backups on average transaction response time.

  37. Questions?

  38. Appendix

  39. FAULT-TOLERANCE EXPERIMENT TEST BED

  40. 0 Extended UDDI WFS <context xsd:type="ContextType"timeout=“100"> <context-service>http://.../HPSearch</ context-service> <content> HPSearch associated additional data generated during execution of workflow. </content> </context> <context xsd:type="ContextType"timeout=“100"> <context-service>http://.../WMS</ context-service> <activity-list mustUnderstand="true" mustPropagate="true"> <service>http://.../WMS</service> <service>http://.../HPSearch</service> </activity-list> </context> <context xsd:type="ContextType"timeout=“100"> <context-service>http://.../HPSearch</ context-service> <parent-context>http://../abcdef:012345<parent-context/> <content> profile information related WMS </content> </context> <context xsd:type="ContextType"timeout=“100"> <context-service>http://.../HPSearch</ context-service> <parent-context>http://../abcdef:012345<parent-context/> <content> shared data for HPSearch activity </content> <activity-list mustUnderstand="true" mustPropagate="true"> <service>http://.../DataFilter1</service> <service>http://.../PICode</service> <service>http://.../DataFilter2</service> </activity-list> </context> <context xsd:type="ContextType"timeout=“100"> <context-id>http://../abcdef:012345<context-id/> <context-service>http://.../HPSearch</ context-service> <content>http://danube.ucs.indiana.edu:8080\x.xml</content> </context> 2 3 4 WMS 1 http://..../..../..txt WMS Client 7,8,9 6 Data Filter HP Search PI Code 5,11 10 Data Filter http://..../..../x.gml Context Information Service The Pattern Informatics GIS-SOA based workflow application session shared state service associated user profile activity <?xml version="1.0" encoding="UTF-8"?> <soap:Envelope xmlns:soap="http://www.w3..."> <soap:Header encodingStyle=“URL" mustUnderstand="true"> <context xmlns=“ctxt schema“ timeout="100"> <context-id>http..</context-id> <context-service> http.. </context-service> <context-manager> http.. </context-service> <activity-list mustUnderstand="true" mustPropagate="true"> <p-service>http://../WMS</p-service> <p-service>http://../HPSearch</p-service> </activity-list> </context> </soap:Header> SOAP header for Context Dynamic Metadata Examples for a GIS Workflow 5,6: WMS starts a session, invokes HPSearch to run workflow script for PI Code with a session id 7,8,9: HPSearch runs the workflow script and generates output file in GML format (& PDF Format) as result 10: HPSearch writes the URI of the of the output file into Context 11: WMS polls the information from Context Service 12: WMS retrieves the generated output file by workflow script and generates a map

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