Forecasting Network Throughput of Remote Data Access in Computing Grids.

Computing grids are key enablers of computational science. Researchers from many fields (High Energy Physics, Bioinformatics, Climatology, etc.) employ grids for execution of distributed computational jobs. These computing workloads are typically data-intensive. The current state of the art approach for data access in grids is data placement: a job is scheduled to run at a specific data center, and its execution commences only once the complete input data has been transferred there. An alternative approach is remote data access: a job may stream the input data directly from arbitrary storage elements. Remote data access brings two innovative benefits: (1) the jobs can be executed asynchronously with respect to the data transfer; (2) when combined with data placement on the policy level, it can aid in the optimization of the network load, since these two data access methodologies partially exhibit nonoverlapping bottlenecks. However, in order to employ this technique systematically, the properties of its network throughput need to be studied carefully. This paper presents experimentally identified parameters of remote data access throughput, statistically tested formalization of these parameters and a derived throughput forecasting model. The model is applicable to large computing workloads, robust with respect to arbitrary dynamic changes in the grid infrastructure and exhibits a long-term prediction horizon. Its purpose is to assist various stakeholders of the grid in decision-making related to data access patterns. This work is based on measurements taken on the Worldwide LHC Computing Grid at CERN.