January 1999 CHAIMS1 Objectives C H A I M S CLAM CPAM Scheduling ESTIMATE EXTRACT Provide high-level, composition-only language (or graphical front-end)

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Presentation transcript:

January 1999 CHAIMS1 Objectives C H A I M S CLAM CPAM Scheduling ESTIMATE EXTRACT Provide high-level, composition-only language (or graphical front-end) for non- technical domain experts. Exploit optimization possibilities in composing remote services. Compose remote services in a way that takes into account special characteristics of such services like autonomy, distribution, heterogeneity, and cost.

January 1999 CHAIMS2 Typical Scenario - Logistics A general has to ship troops and/or various material from L.A. to Chicago: –different kind of material, not every airport equally suited –congestion, prices, weather constraints –exact due or ready dates –different transport service providers Today: ·calling different companies, looking up information on the web, reservations by hand ·hand coded systems Future: fast system development by tools supporting automated composition

January 1999 CHAIMS3 Approach Composition of megamodules (large, autonomous, distributed, heterogeneous services) by the composition only language CLAM and the access protocol CPAM that also provide run-time cost estimation and allow automatic run-time invocation scheduling. Composition is automated by using the protocol CPAM on top of several distribution systems by hiding protocol details in CLAM, and by providing a compiler for CLAM.

January 1999 CHAIMS4 CHAIMS proves that... »We can do composition in a high-level language and hide technical details »Large-scale composition can be automated »Run-time cost estimation is essential for invocation scheduling optimization.

January 1999 CHAIMS5 Focus for Future Applying CHAIMS to a larger real-life example. Automated scheduling of invocations and extractions, automatic optimization of dataflows. Automatic generation of direct dataflows between megamodules. Flexible interaction with megamodules; extracting and handling overview results. Enhancing CHAIMS language CLAM and complementing it by graphical front-end.