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UID:submissions.supercomputing.org_SC24_sess750_ws_indis117@linklings.com
SUMMARY:Framework for Integrating Machine Learning Methods for Path-Aware 
 Source Routing
DESCRIPTION:Anees Al-Najjar (Oak Ridge National Laboratory (ORNL)); Doming
 os Paraiso (Federal University of Espírito Santo, Department of Informatic
 s); Mariam Kiran (Oak Ridge National Laboratory (ORNL)); Cristina Dominici
 ni, Everson Borges, Rafael Guimaraes, and Magnos Martinello (Federal Unive
 rsity of Espírito Santo, Department of Informatics); and Harvey Newman (Ca
 lifornia Institute of Technology)\n\nSince the advent of software-defined 
 networking (SDN), Traffic Engineering (TE) has been highlighted as one of 
 the key applications that can be achieved through software-controlled prot
 ocols. TE problems involve difficult decisions such as allocating flows, e
 ither via splitting them among multiple paths or by using a reservation sy
 stem, to minimize congestion. However, creating an optimized solution is c
 umbersome and difficult as traffic patterns vary and change with network s
 cale, capacity, and demand. AI methods can help alleviate this by finding 
 optimized TE solutions for the best network performance. In this paper, we
  leverage Hecate to practically demonstrate TE on a real network, collabor
 ating with PolKA, a source routing protocol tool. With real-time traffic s
 tatistics, Hecate uses this data to compute optimal paths that are then co
 mmunicated to PolKA to allocate flows. This work proves valuable for truly
  engineered self-driving networks helping translate theory to practice.\n\
 nTag: Architecture, Data-Intensive, Network, Performance Optimization, Qua
 ntum Computing, Security, System Administration\n\nRegistration Category: 
 Workshop Reg Pass\n\nSession Chairs: Akbar Kara (Ciena Corporation, SCinet
 ); Anees Al-Najjar (Oak Ridge National Laboratory (ORNL), SCinet); and Nik
  Sultana (Illinois Institute of Technology, SCinet)\n\n
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