Abstract
Grid computing was developed to provide users with uniform access to large-scale distributed resources. This has worked well, however there are significant resources available to the scientific community that do not follow this paradigm - those on cloud infrastructure providers, HPC supercomputers or local clusters. DIRAC (Distributed Infrastructure with Remote Agent Control) was originally designed to support direct submission to the Local Resource Management Systems (LRMS) of such clusters for LHCb, matured to support grid workflows and has recently been updated to support Amazon's Elastic Compute Cloud.
This raises a number of new possibilities - by opening avenues to new resources, virtual organisations can change their resources with usage patterns and use these dedicated facilities for a given time.
For example, user communities such as High Energy Physics experiments, have computing tasks with a wide variety of requirements in terms of CPU, data access or memory consumption, and their usage profile is never constant throughout the year. Having the possibility to transparently absorb peaks on the demand for these kinds of tasks using Cloud resources could allow a reduction in the overall cost of the system.
This paper investigates interoperability by following a recent large-scale production excercise utilising resources from these three different paradigms, during the 2010 Belle Monte Carlo run. Through this, it discusses the challenges and opportunities of such a model.
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