The rise of cognitive networks for data centre interconnect
Technology software is rapidly growing and is facilitating cost reduction among many operations, adjusting a deep focus on the potential of analytics and machine learning. Analytics and machine learning’s ability to control, examine and manage software networks has directed a shift towards cognitive networks.
The expectation of technology performance is rapidly growing among applications including interactive game streaming, 4K enhanced television, streaming television and others which are all enhancing the compound annual growth rate (CAGR). CAGR is a mathematical equation representing the rate of return necessary for an investment to grow from its opening balance to its final balance, providing investors with the return they receive from a particular investment period. The rapidly growing CAGR traffic is transferring through data centre interconnect networks which has influenced it to grow into a very significant market. It is necessary for service and content providers to enhance their operation efficiencies and gain more from current infrastructure in order to manage the rapid significant growth of data centre interconnect networks.
At the optical layer, a great amount of this has already been resolved, allowing the network to be more reconfigurable and adaptable than it previously has been. Through leveraging flexible symbol rates, modulation and grid options we have experienced substantial balance capacity versus distance improvements. We have further seen significant amounts of data collated with knobs to drive a greater dynamic optical layer, reducing the overall network cost.
Software advances and an emphasis across analytics and machine learning is required for the cost reduction to be facilitated. Through software’s increasing ability to control, examine and manage the network, a shift has been directed to cognitive network applications. Cognitive networks comprise a type of data network which makes use of cutting-edge technology to resolve current issues fronting networks, through utilising technology research areas.
Cognitive networks are interrelated with artificial intelligence, comprising a link for the platform to gather, understand and work on the network. In doing so a leveraging machine learning tool is required for the purpose of collecting and delivering the data to a software, which then transfers the proper corrective action to perform network adjustments. Machine learning obtains the ability to learn a network and associate its procedures, plans and actions for correct performance issues or events through the software defined networks.
Artificial Intelligence is rising rapidly, responding and reconfiguring networks efficiently. It obtains the ability to eliminate costs by not requiring a content provider to oversee and interevent the process. To meet this potential, dynamic learning tools and machine learning algorithms are necessary.
Dynamic learning obtains the ability to feed activity back to the operator. This would then allow us to minimise network margins whilst providing optimised data rates throughout the data centre. Dynamic learning will enhance this through calculating proper amplifier settings as well as the modulation technique. With the growth of technological development, we are expecting to see the margins in dynamic learning merge together and reduce the general cost of the network.
Cognitive networks can help to minimise network traffic by being able to restore or predict network breakdowns. This machine learning software is able to adjust and reroute the traffic to ensure the impact from the network failure is minimised, reducing the possible overall cost.
Dynamic learning and cognitive networks are essential for improving operational efficiencies. With the rising amount of available data networks, it is difficult to analyse the accuracy and relevance of data which is used, as well as whether the correct correlations of the correct parameters are being utilised.
The industry still requires the general data collection method to be completed for now, to ensure the machine learns the accurate and relevant methods. Machines learn through the collected data sharing a mutual format, which is found through the common data model. Service providers and content providers are notably pushing towards driving common model creations as a result.
Moving forward, identifying which anomalies are to be captured is an essential step to ensuring only relevant data is collated. Leveraging cognitive networks provides the opportunity to perform more efficiently towards customer issues and effectively minimises costs.
VIAVI recognises the increasing use of cognitive networks, and can enable customers to a passive optical network. Partner with VIAVI to discover how the rise of cognitive networks can assist with your data centres.