Simulations and study of a new scheduling approach for distributed data production
| Autoři | |
|---|---|
| Rok publikování | 2016 |
| Druh | Článek ve sborníku |
| Konference | Journal of Physics: Conference Series, vol. 762 |
| Fakulta / Pracoviště MU | |
| Citace | |
| Doi | http://dx.doi.org/10.1088/1742-6596/762/1/012023 |
| Obor | Informatika |
| Klíčová slova | data transfer planning; distributed data processing; Grid; network flows; data production |
| Popis | Distributed data processing has found its application in many fields of science (High Energy and Nuclear Physics (HENP), astronomy, biology to name only those). We have focused our research on distributed data production, an essential part of computations in HENP. Using our previous experience, we have recently proposed a new scheduling approach for distributed data production which is based on the network flow maximization model. It has a polynomial complexity providing required scalability with respect to the size of computations. Our approach improves the overall data production throughput due to three factors: transfer input files in advance before their processing (allows to decrease I/O latency); balancing of the network traffic (includes splitting the load between several alternative transfer paths); and transfer files sequentially in a coordinated manner (allows to reduce the influence of possible network bottlenecks). In this contribution, we present the results of our new simulations based on the GridSim framework which is one of the commonly used tools in the field of distributed computations. In these simulations we study the behavior of standard scheduling approaches compared to our recently proposed approach in a realistic environment relying on the data from the STAR and ATLAS experiments and considering the influence of the background traffic. The final goal of the research is to integrate the proposed scheduling approach into the real data production framework. In order to achieve this we are constantly moving our simulations towards real use cases, study scalability of the model and the influence of the scheduling parameters on the quality of the solution. |
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