Abstract
Data-aware scheduling in large-scale heterogeneous computing systems remains a challenging research issue, especially in the era of Big Data. Design of all data-related components of the popular distributed environments, such as Data Clouds (DCs), Data Grids (DGs) and Data Centers supports the processing, analysis and monitoring of the big data generated by various sources at computing centers by the end-users, devices and services. The above facts leave no doubts that data scheduling must be integrated in a single joint process together with the scheduling of computer tasks and applications. Therefore, many of the current optimization issues need to be changed and new requirements have to be considered in the scheduling process. This includes data transmission times, data processing times, availability of the data servers, safety and authentication in the data access processes. This paper presents a new version of the Expected Time to Compute Matrix model (ETC Matrix) for the case of data-aware independent batch scheduling in physical network in DGs and DCs environments. Simple geneticbased schedulers have been developed for experimental justification of the significance of the presented problem.
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