NetAIM 2018 - 2018 Workshop on Network Meets AI & ML (NetAIM 2018)

Distributed processing systems for Artificial Intelligence (AI) and Machine Learning (ML), such as Hadoop, Spark, Storm, GraphLab, TensorFlow etc., are widely used by industry. On the one hand, networking is a well-known bottleneck for AI & ML systems. For example, traffic patterns experienced by ML applications during training can be optimized using techniques such as parameter servers and vertex-cut for graphs. New technologies are also being embedded in the network; examples include RDMA over converged Ethernet (RoCE) and GPU direct. On the other hand, the ever increasing complexity of networks makes effective monitoring, modeling, auditing, and overall control of network traffic difficult if not impossible. Hence there is a need for more powerful methods to solve the challenges faced in network design, deployment, and management. AI & ML have been successfully applied to various perceptual domains, including computer vision, natural language processing, and voice recognition. In addition, ML techniques are showing impressive results in new domains such as medicine, finance and astronomy, to name a few. This success in non-perceptual domains suggests that ML techniques could be successfully applied to problems in the network space. The networking community should look upon all these challenges as their opportunities in the Machine Learning era.
Call for Papers
NetAIM 2018 provides a venue for presenting innovative ideas to discuss future research agendas in computer networking of/by/for AI & ML systems. We look for submissions of previously unpublished work on topics including, but not limited to, the following:
telemetry in DC
network measurement
closed loop control
AI & ML algorithm for network scheduling and control
self learning network architecture and system
services identify with AI & ML
traffic engineer with AI & ML
congestion control based on AI & ML
network security based on AI & ML
network verification
network QoS based on AI & ML
measurement and analysis of network traffic for AI & ML systems
optimizing AI & ML systems with new networking options (e.g., RDMA, NVLink)
AI & ML application-driven networking optimization (e.g., MPI offload)
networking AI & ML computation with large-scale storage technologies (e.g., NVMe over Fabric, FCoE)
networking for distributed ML systems
networking for large-scale distributed graph processing systems
new network functionality for AI & ML system ( e.g., computation in network )
application-driven network architecture design