Optical networking, employing wavelength division multiplexing (WDM), is seen as the technology of the future for the Internet. This dissertation investigates several important problems affecting optical circuit switching (OCS) and optical burst switching (OBS) networks. Novel algorithms and new approaches to improve the performance of these networks through effective routing and channel scheduling are presented. Extensive simulations and analytical modeling have both been used to evaluate the effectiveness of the proposed algorithms in achieving lower blocking probability, better fairness as well as faster switching. The simulation tests were performed over a variety of optical network topologies including the ring and mesh topologies, the U.S. Long-Haul topology, the Abilene high-speed optical network used in Internet 2, the Toronto Metropolitan topology and the European Optical topology. Optical routing protocols previously published in the literature have largely ignored the noise and timing jitter accumulation caused by cascading several wavelength conversions along the lightpath of the data burst. This dissertation has identified and evaluated a new constraint, called the wavelength conversion cascading constraint. According to this constraint, the deployment of wavelength converters in future optical networks will be constrained by a bound on the number of wavelength conversions that a signal can go through when it is switched all-optically from the source to the destination. Extensive simulation results have conclusively demonstrated that the presence of this constraint causes significant performance deterioration in existing routing and wavelength assignment (RWA) algorithms. Higher blocking probability and/or worse fairness have been observed for existing RWA algorithms when the cascading constraint is not ignored. To counteract the negative side effect of the cascading constraint, two constraint-aware routing algorithms are proposed for OCS networks: the desirable greedy algorithm and the weighted adaptive algorithm. The two algorithms perform source routing using link connectivity and the global state information of each wavelength. Extensive comparative simulation results have illustrated that by limiting the negative cascading impact to the minimum extent practicable, the proposed approaches can dramatically decrease the blocking probability for a variety of optical network topologies. The dissertation has developed a suite of three fairness-improving adaptive routing algorithms in OBS networks. The adaptive routing schemes consider the transient link congestion at the moment when bursts arrive and use this information to reduce the overall burst loss probability. The proposed schemes also resolve the intrinsic unfairness defect of existing popular signaling protocols. The extensive simulation results have shown that the proposed schemes generally outperform the popular shortest path routing algorithm and the improvement could be substantial. A two-dimensional Markov chain analytical model has also been developed and used to analyze the burst loss probabilities for symmetrical ring networks. The accuracy of the model has been validated by simulation. Effective proactive routing and preemptive channel scheduling have also been proposed to address the conversion cascading constraint in OBS environments. The proactive routing adapts the fairness-improving adaptive routing mentioned earlier to the environment of cascaded wavelength conversions. On the other hand, the preemptive channel scheduling approach uses a dynamic priority for each burst based on the constraint threshold and the current number of performed wavelength conversions. Empirical results have proved that when the cascading constraint is present, both approaches would not only decrease the burst loss rates greatly, but also improve the transmission fairness among bursts with different hop counts to a large extent.

To test Cloud RAN applications, such as the virtual distributed unit (vDU) and centralized virtual unit (vCU), a test environment is required, commonly known as a “test bed” or “test channel”. This test bed comprises various cloudnative infrastructures, including different hardware and software components. Each test bed possesses distinct capacities for testing various features, leading to varying costs. With the increasing number of cloud applications, additional test beds are necessary to ensure thorough testing before releasing these applications to the market. To optimize the creation process of a Cloud-native test bed, leveraging artificial intelligence and machine learning approaches can be beneficial. This thesis presents, applies, and evaluates an AI-based approach for optimizing the construction of Cloud-native test beds. The proposed solution’s feasibility is assessed through an empirical evaluation conducted in the Telecom domain at Ericsson AB in Sweden. / För att testa Cloud RAN-applikationer, såsom en virtuell distribuerad enhet (vDU) och en centraliserad virtuell enhet (vCU), kan en testmiljö behövas, som också kallas för ”testbädd” eller ”testkanal”. En testbädd inkluderar vanligtvis olika molnbaserade infrastrukturer såsom olika hårdvaru- och mjukvarukomponenter. Varje testbädd kan ha olika kapaciteter som används för att testa olika funktioner och därigenom ha olika kostnader. I takt med att antalet molnapplikationer ökar kan det krävas fler testbäddar för att testa molnapplikationernas funktioner innan de släpps på marknaden. Genom att använda olika artificiell intelligens och maskininlärningsmetoder kan vi optimera byggprocessen av en molnbaserad testbädd. I denna avhandling introducerar, tillämpar och utvärderar vi en AI-baserad metod för att optimera byggprocessen av molnbaserade testbäddar. Genomförbarheten av den föreslagna lösningen studeras genom en empirisk utvärdering som har utförts inom telekomområdet på Ericsson AB i Sverige.