Grant: Discovery Grants Program - Individual

Network management is the process of monitoring and controlling a network so it continues providing its services efficiently. Traditional network management heavily relies on human operators, who are costly, error-prone, and slow to adapt to changes. It cannot cope with the complexity due to the increasing number and diversity of network devices, and the stringent requirements of emerging applications. Automated management has been the holy grail of network management research for decades. It aims at achieving autonomous networks, i.e., networks capable of autonomously monitoring their status, analyzing problems, making decisions, and executing corrective actions. Despite several attempts to achieve autonomous networks in the past, their practical deployments have largely remained unrealized. Several factors contribute to this, including the existence of many stakeholders with conflicting goals, reliance on proprietary hardware, the inability to process network monitoring data at scale, and the lack of global visibility restricting network-wide optimizations. The stars are now aligned to realize the vision of autonomous networks thanks to (i) advances in network softwarization (an emerging paradigm where software controls the treatment of network flows, adds value to these flows by software processing, and orchestrates network services to meet application needs); (ii) recent breakthroughs in machine learning; and (iii) the availability of large-scale data processing platforms. However, a number of fundamental challenges must be addressed in order to create the synergy between these different technology domains and achieve automated orchestration and management of softwarized networks. The long-term goal of this research program is to address key management challenges for realizing the vision of autonomous networks. Specifically, my team and I will pursue three primary objectives: � Leverage network softwarization and large-scale data streaming to develop: (i) efficient algorithms for collecting network data at minimal overhead; and (ii) customizable monitoring probes for detecting a wider range of network events based on changing network conditions. � Leverage recent innovations in large-scale machine learning for real-time analytics of monitoring data to extract knowledge, infer the behavior of the network over time, and automatically make management decisions. � Leverage optimization techniques and service-oriented architectures to develop novel algorithms and systems for efficient, resilient, and dynamic orchestration of network services. Realizing the vision of autonomous networks will contribute to reducing network operators' capital and operational expenditures, eliminating network outages, optimizing network operations, and deploying innovative services faster. The proposed research program will produce the trainees required to drive the coming wave of network innovation and ensure Canada will continue in its leadership role.