Dynamic Adaptation of Parallelism Level in Data Transfer Scheduling

We discuss dynamic parameter tuning in wide-area data transfers for efficient utilization of available network capacity and optimized end-to-end application performance.Impacts of parallel TCP streams as well as concurrent data transfer jobs running simultaneously have been studied.We present an adaptive approach for tuning parallelism level of data placement jobs in distributed environments.The adaptive data scheduling includes dynamically setting parameters of data placement jobs.The proposed methodology operates without depending on any external profiles to adapt to changing network conditions.