Early error detection and classification in data transfer scheduling

Data transfer in distributed environment is prone to frequent failures resulting from back-end system level problems, like connectivity failure which is technically untraceable by users. Error messages are not logged efficiently, and sometimes are not relevant/useful from users' point-of-view. Our study explores the possibility of an efficient error detection and reporting system for such environments. Besides, early error detection and error classification have great importance in organizing data placement jobs. It is necessary to have well defined error detection and error reporting methods to increase the usability and serviceability of existing data transfer protocols and data management systems. Prior knowledge about the environment and awareness of the actual reason behind a failure would enable data placement scheduler to make better and accurate decisions. We investigate the applicability of proposed early error detection and error classification techniques to improve arrangement of data placement jobs and to enhance decision making of data placement schedulers.