Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G's diverse use cases. As machine learning (ML) is expected to play a pivotal role in the efficient control and management of these networks, in this work we examine the ML-based Quality-of-Transmission (QoT) estimation problem under the dynamic network slicing context, where each slice has to meet a different QoT requirement. We examine ML-based QoT frameworks with the aim of finding QoT model s that are fine-tuned according to the diverse QoT requirements. Centralized and distributed frameworks are examined and compared according to their accuracy and training time. We show that the distributed QoT models outperform the centralized QoT model, especially as the number of diverse QoT requirements increases.
Several ML applications have been already developed and explored for optical network planning purposes @cite_9 @cite_8 . In general, the state-of-the-art assumes that the optical network is centrally controlled by an SDN-based optical network controller @cite_11 @cite_20 , equipped with storage, processing, and monitoring capabilities (Fig. ). The main responsibility of the SDN-based controller is to efficiently manage the network resources in such a way that the diverse QoS requirements of the different use cases are met, as closely as possible, by the virtual network topology (VNT). The VNT can be viewed as a single slice (virtual network) that has to best fit all the diverse use cases.
@cite_9 @cite_11 @cite_20 @cite_8
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Software-defined networking (SDN) has been proposed as a next-generation control and management framework, facilitating network programmability to address emerging dynamic application requirements. The separation of control and data planes in SDN demands the synergistic operation of the two entities for globally optimized performance. We identify the collaboration of the control plane and the data plane in software-defined optical transmission systems as a cyber-physical interdependency where the "physical" fiber network provides the “cyber” control network with means to distribute control and signaling messages and in turn is itself operated by these "cyber" control messages. We focus on the cyber-physical interdependency in SDN optical transmission from a network robustness perspective and characterize cascading failure behaviors. Our analysis suggests that topological properties pose a significant impact on failure extensibility. We further evaluate the effectiveness of optical layer reconfigurability in improving the resilience of SDN controlled optical transmission systems. Modern planetary-scale online services have massive data to transfer over the wide area network (WAN). Due to the tremendous cost of building WANs and the stringent timing requirement of distributed applications, it is critical for network operators to make efficient use of network resources to optimize data transfers. By leveraging software-defined networking (SDN) and reconfigurable optical devices, recent solutions design centralized systems to jointly control the network layer and the optical layer. While these solutions show it is promising to significantly reduce data transfer times by centralized cross-layer control, they do not have any theoretical guarantees on the proposed algorithms. This paper presents approximation algorithms and theoretical analysis for the online transfer scheduling problem over optical WANs. The goal of the scheduling problem is to minimize the makespan (the time to finish all transfers) or the total sum of completion times. We design and analyze various greedy, online scheduling algorithms that can achieve 3-competitive ratio for makespan, 2-competitive ratio for minimum sum completion time for jobs of unit size, and 3α-competitive ratio for jobs of arbitrary transfer size and each node having degree constraint d, where α = 1 when d = 1 and α = 1.86 when d ≥ 2. We also evaluated the performance of these algorithms and compared the performance with prior heuristics. Bulk transfer on the wide-area network (WAN) is a fundamental service to many globally-distributed applications. It is challenging to efficiently utilize expensive WAN bandwidth to achieve short transfer completion time and meet mission-critical deadlines. Advancements in software-defined networking (SDN) and optical hardware make it feasible and beneficial to quickly reconfigure optical devices in the optical layer, which brings a new opportunity for traffic management on the WAN. We present Owan, a novel traffic management system that optimizes wide-area bulk transfers with centralized joint control of the optical and network layers. can dynamically change the network-layer topology by reconfiguring the optical devices. We develop efficient algorithms to jointly optimize optical circuit setup, routing and rate allocation, and dynamically adapt them to traffic demand changes. We have built a prototype of Owan with commodity optical and electrical hardware. Testbed experiments and large-scale simulations on two ISP topologies and one inter-DC topology show that completes transfers up to 4.45x faster on average, and up to 1.36x more transfers meet their deadlines, as compared to prior methods that only control the network layer. This paper presents new Software Defined Networking (SDN) control framework for Quality of Service (QoS) provisioning. The proposed SDN controller automatically and flexibly programs network devices to provide required QoS level for multimedia applications. Centralized control monitors state of the network resources and performs smart traffic management according to collected information. Beside QoS provisioning for priority flows, the proposed solution aims at minimizing degradation of best-effort traffic. The experimental results show significant performance improvement under high traffic load compared to traditional best-effort and IntServ service models.