Ciena is a Canadian company leader in engineering and manufacturing networking systems and devices. The company has around 5,000 operable products in its portfolio. The vast majority of Ciena products generate logs during the boot up and the mission mode operations from the various tasks running on their real time operating systems. The company wants thus to increase its software’s capabilities in order to be able to collect any type of log data generated in the production site and linked to other external information to extract actionable knowledge.
This project will address the production test needs of a silicon-based high-speed photonic transceiver solution for metro-reach terabit optical modems. For this project, the partnership will be between Ciena, DA Integrated (test development contractor) and Prof. Gordon W. Roberts from McGill University (academic expert in DFT and mixed-signal design and test).
Networks are moving towards being adaptive. This means that automation will be used to replace processes which are today highly manual. This project proposes a development of knowledge in the area of algorithms required to enable adaptive networks. The project will train two PhD students to understand optical networks and devise optimization algorithms in the areas of interest. In particular, the algorithms will be devised to be fast and near-optimal to enable their implementation in the network in accordance with operatorâs goals of making the network near-optimal and adaptive.
The purpose of this project is to investigate self-adaptive forecasting and anomaly prediction algorithms based on deep neural networks (DNNs). DNNs present a compelling technology due to their wide-spread availability through open-source projects (e.g. TensorFlow, MXNet). However, usability of DNNs in scenarios outside of image, speech or text pattern recognition is mostly unproven. This project aims to reduce the knowledge gap that exists in the usage of DNNs in the context of pattern recognition with DNNs in network management and network equipment manufacturing.
Networks are moving towards being adaptive. This means that automation will be used to replace processes which are today highly manual. This project proposes a development of knowledge in the area of algorithms required to enable adaptive networks. The project will train two PhD students to understand optical networks and devise optimization algorithms in the areas of interest. In particular, the algorithms will be devised to be fast and near-optimal to enable their implementation in the network in accordance with operators goals of making the network near-optimal and adaptive.
Product documentation is an important information tool connecting any business to its end-users and customers. Comprehensive product documentation will likely result in positive evaluation of the products by the customers and may influence their future purchasing decisions. Many studies show product documentation remains an essential element of any new product even for modern electronic devices used for information and communication technologies.
The research will consist of exploring a new language as well as a new paradigm shift in the orchestration and analytics involved in operating a Fiber optical infrastructure equipped with IP routers and Computers. These computers will be equipped with programmable devices that will allow further instructions and detailing about the next generation of internets emerging services. These services require more automation and more analytics to become more adaptive if not autonomous. We will research how autonomous can these networks get by involving this new programmable language called P4.
Ciena would like to drastically improve its ability to track, analyse and forecast the cost of products. Building on experience with the current tool suite and business practices, Ciena aims to develop a next-generation product cost analytics solution that will run on its existing IT infrastructure and allow capture, analysis and modeling of historical as well as simulated material and transformation cost data.
Project NOVA will build on the University of Ottawa and Ciena’s advanced analytics capabilities to allow networks around the world to understand where video flows run over their network. This will allow the network operators to improve video Qualify of Experience for their end customers, more quickly and cost effectively fix video impacting network problems, plan their networks to better support video, and provide greater customer service awareness of end customer over the top video quality. Ciena anticipates this capability will propel it into be the world leader in network video analytic
System performance can be analyzed by measuring its operation, and by studying a performance model. Each has advantages: measurements have fidelity to the actual system, while models have predictive power. This work will join the two approaches, by creating a model from data collected from traces. If successful, this model will help Ciena to understand performance issues, and to maintain or improve performance as the system evolves.