Classification and forecasting of real-time server traffic flows employing long short-term memory for hybrid E/O data center networks
Mihail Balanici, Stephan Pachnicke
Abstract
Long short-term memory neural networks demonstrate a classification accuracy larger than 99% for highly variable and bursty, real-time server traffic flows. Their performance in terms of forecasting precision displays promising results, both for one-step as well as multi-step predictions. These capabilities make the a priori detection of heavy data streams possible, thus enabling the employment of optical circuit switching.
Topics & Concepts
Computer scienceReal-time computingTerm (time)A priori and a posterioriLong short term memoryArtificial neural networkData centerComputer networkArtificial intelligenceRecurrent neural networkPhilosophyPhysicsQuantum mechanicsEpistemologyOptical Network TechnologiesAdvanced Optical Network TechnologiesNeural Networks and Reservoir Computing