Universal Global Adaptive routing (UGAL) is a common routing scheme used in systems based on the Dragonfly interconnect topology. UGAL uses information about local link-loads to make adaptive routing decisions. Traffic Pattern-based Adaptive Routing (TPR) enhances UGAL by incorporating additional network statistics into the routing process. Contemporary switches are designed to accommodate an expansive set of network performance metrics. Distinguishing between significant, predictive metrics and insignificant metrics is critical to the process of designing an adaptive routing algorithm. We propose the use of recurrent neural networks to assess the relative predictive power of various network statistics. Using this method we rank the predictive power of network statistics using data collected from a network simulator. Both UGAL and TPR require tuning of hyper-parameters to achieve optimal performance, with TPR having more than 20 parameters for the Cray Cascade architecture. We demonstrate that the optimal value of these parameters can vary significantly based on the size of the architecture, the arrangement of global links chosen for the Dragonfly topology, and the traffic that the system will likely encounter. We propose and evaluate using a neural network to simplify the tuning of hyper-parameters used in TPR. We find that this approach is able to match or exceed the performance of TPR across several synthetic traffic patterns using a network simulator. / A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science. / Spring Semester 2019. / April 22, 2019. / adaptive routing, adaptive systems, dragonfly network, machine learning, network topology, routing / Includes bibliographical references. / Xin Yuan, Professor Co-Directing Thesis; Xiuwen Liu, Professor Co-Directing Thesis; Piyush Kumar, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_709819 |
| Contributors | Ryasnianskiy, Yevgeniy (author), Yuan, Xin (Professor Co-Directing Thesis), Liu, Xiuwen (Professor Co-Directing Thesis), Kumar, Piyush (Committee Member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Computer Science (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, master thesis |
| Format | 1 online resource (37 pages), computer, application/pdf |
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