michael d. cohenschool of information

university of michigan19 June 02003

Remarks on the GriPhyN & iVDGL

Collaboratories

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)

“Physics Emulation”

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?)

Economics of the grid

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

Infrastructure

‘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 }

Virtual Data