Remarks on the GriPhyN & iVDGL Collaboratories

1. Remarks on the GriPhyN & iVDGLCollaboratories • michael d. cohen • school of information • university of michigan • 19 June 02003

2. “Physics Emulation” • all physics is not high-energy physics (consider cosmology; how/what can the general collab field learn frm the physics examples?) • learning about collaboratory success from (h-e) physics requires more than knowing the strategies used (what are the prereq features that have to be present for the strategies to work?) • dissecting the prerequisites of the strategies and assessing their transfer to other fields • high-paradigm, high (self-)esteem, long organizational tradition, large scale,high exit rates(physicists converge strongly on fundamental concepts, methods, even heros;society admires h-e-physics; they think there are few things they can’t do; h-e-physics has a long string of org’l successes at increasing scales; large scale creates scale-down question: will it work for smaller projects, that can fail; h-e physics has unusual career demography with much exit and few top roles)

3. Economics of the grid • cycle supply estimates made under uncertainty about eventual number of grid communities • (will the estimated number ofcycles still be available when many projects of many fieds move to the grid) • local incentives for cycle contributions - upgrade cycles (will local users upgrade more often if they experience grid-related loads? will NSF pay for harware that provides grid cycles or storage?)

4. Infrastructure • development strategies • “pipelining” - new dependence on foresight rather than history (our old system for evolving infra-structure let a stage settle in use then moved a portion to infrastructure (say moving application functionality to op system; in the model that Erik Hofer described as emerging that wisdom of experience is b eing replaced by foresight about what should be standardized. ) • issues of granularity, innovation constraint

5. Virtual Data • ‘data provenance’ has extremely high potential import - compare Mars tapes(we know-from Latour-style studies that lots of the subtlety of of science is in data transformation processes and rationales. reproducing prior results and comparison are fundamental; taped data of the Mars missions of early 70s no longer accessible- not just media and ardware, also op systems and applications ) • dependence on platform homogeneity & stability (GLUE: Grid Lab Uniform Env.) ?(for GriPhyN VDT to succeed platform stability needs to exist cross-sectionally and over time otherwise 20 years later you can’t compare new experiment to old) • difficulties of documenting rationales of transformations(these records will become artifacts in scientific processes: finding who designed to transform,recovering argument for why calibration needs correction, say) {so the talk identifies some fields we need to develop (econOfGrid,InfraStruSynamics, along with issues of transferring experience between fields and in the science uses of data }