We employ a Digital Twin of the optical network to improve the power commissioning/optimization process by first figuring out a safe and efficient power adjustment plan through simulations before applying them on the real live network. The proposed method can find the optimal configuration and the sequence of adjustments required, and will avoid applying unnecessary or provisional changes to the live network as well as the potential rollback of those changes.

5G transport networks will support dynamic services with diverse requirements through network slicing. Elastic Optical Networks (EONs) facilitate transport network slicing by flexible spectrum allocation and tuning of transmission configurations. A major challenge in supporting dynamic services is the lack of priori knowledge of future slice requests. As a consequence, slice embedding can become sub-optimal over time, leading to spectrum fragmentation and skewed utilization. This in turn can block future slice requests, impacting operator revenue. To address this issue, operators can periodically re-optimize slice embedding for reducing fragmentation. In this paper, we address this problem of re-optimizing network slice embedding on EONs for minimizing fragmentation. The problem is solved in its splittable version, which significantly increases problem complexity, but also offers more opportunities for a larger set of re-configuration actions. We employ simulated annealing for systematically exploring the large solution space. We also propose a greedy algorithm to address the practical constraint of limiting the number of re-configuration steps. Moreover, we present a novel method based on Deep Reinforcement Learning (DRL) for determining when performing re-configuration is most effective. Our extensive simulations demonstrate that the greedy algorithm yields a solution very close to that obtained using simulated annealing while requiring orders of magnitude lesser re-configuration actions. Finally, we show that by applying the greedy algorithm periodically on the network according to the DRL-based time selection algorithm, a significant improvement in the total number of accepted slice requests can be achieved with only performing a limited number of re-configuration operations.

Optical transmission performance measurement and prediction are key Digital Twin capabilities for the optical network. Recent advances in instrumentation and models that support optical transmission performance assessment are presented. Optical network operations automation use cases enabled by a transmission performance-focused Digital Twin are described or demonstrated. These include provisioning automation, transmission performance risk mapping, optimization-based planning and control, and generalized optical margin reduction.

5G transport networks will support dynamic services with diverse requirements through network slicing. Elastic Optical Networks (EONs) facilitate transport network slicing by flexible spectrum allocation and tuning of transmission configurations such as modulation format and forward error correction. A major challenge in supporting dynamic services is the lack of a priori knowledge of future slice requests. In consequence, slice embedding can become sub-optimal over time, leading to spectrum fragmentation and skewed utilization. This in turn can block future slice requests, impacting operator revenue. Therefore, operators need to periodically re-optimize slice embedding for reducing fragmentation. In this paper, we address this problem of re-optimizing network slice embedding on EONs for minimizing fragmentation. The problem is solved in its splittable version, which significantly increases problem complexity, but offers more opportunities for a larger set of re-configuration actions. We employ simulated annealing for systematically exploring the large solution space. We also propose a greedy algorithm to address the practical constraint to limit the number of re-configuration steps taken to reach a defragmentated state. Our extensive simulations demonstrate that the greedy algorithm yields a solution very close to that obtained using simulated annealing while requiring orders of magnitude lesser number of re-configuration actions.

Elastic Optical Networks (EONs) enable finer-grained resource allocation and tuning of transmission configurations for right-sized resource allocation. These features make EONs excellent choice for 5G transport networks supporting highly dynamic traffic with diverse Quality-of-Service (QoS) requirements. 5G network slices are expected to host applications with a dynamic nature (e.g., augmented/virtual reality broadcasting), which will result in slice resource requirement changing over time. The initial resource allocation to network slices has to be adapted to accommodate such changes without causing significant disruption to existing traffic and using minimal additional resources. In this paper, we address the problem of scaling bandwidth demand of network slices on an EON-enabled 5G transport network. In contrast to the state-of-the-art, we do not assume any specific technologies for minimizing disruption when accommodating the scaling request. Rather, we propose an Integer Linear Program (ILP) and a heuristic algorithm for accommodating scaling requests by choosing from a comprehensive set of reconfiguration actions. We carefully design a novel cost model for capturing traffic disruptions and additional resource usage by these different actions. Our extensive simulations using realistic network topologies shed light on the trade-off between additional resource usage and disruption while accommodating slice scaling requests by employing a comprehensive set of reconfiguration actions. Simulation results also show that our heuristic algorithm can find solutions that remain within 10% of ILP-based solutions, while executing several orders of magnitude faster than ILP.

This paper reviews the work of industry organizations that are defining the requirements on, and the details of, transport slicing functionality and models, in support of 5G network slicing automation and assurance. Slicing permits the simultaneous support of multiple service frameworks from a converged infrastructure and is key to the 5G operational vision, but poses challenges related to cross-domain coordination, automation and SLA assurance. Transport network slicing is an important component of the overall network slicing picture, and its mechanics are being defined collaboratively by organizations including 3GPP, ETSI ZSM and NFV, and IETF. Supporting demonstrative implementation is being done in the Linux Foundation ONAP project.

5G network slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segments. In particular, the transport segment is currently evolving in the direction of elastic optical networks (EONs), a new generation of optical networks supporting a flexible optical-spectrum grid and novel elastic transponder capabilities. In this paper, we focus on the reliability of 5G transport-network slices in EON. Specifically, we consider the problem of slicing 5G transport networks,i.e., establishing virtual networks on 5G transport, while providing dedicated protection. As dedicated protection requires a large amount of backup resources, our proposed solution incorporates two techniques to reduce backup resources: (i) bandwidth squeezing,i.e., providing a reduced protection bandwidth than the original request; and (ii) survivable multi-path provisioning. We leverage the capability of EONs to fine tune spectrum allocation and adapt modulation format and forward error correction for allocating spectrum resources. Our numerical evaluation over realistic network topologies quantifies the spectrum savings achieved by employing EON over traditional fixed-grid optical networks, and provides new insights on the impact of bandwidth squeezing and multi-path provisioning on spectrum utilization. One key takeaway from our evaluation is that multi-path provisioning can guarantee up to 40% of the bandwidth requested by a VN during failures by provisioning only 10% additional spectrum resources. This also caused VN blocking ratio for BSR up to 40% to remain very close to that of the no-backup case.

In 5G networks, slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segments. In particular, the transport segment is currently evolving in the direction of the so-called elastic optical networks (EONs), a new generation of optical networks supporting a flexible optical-spectrum grid and novel elastic transponder capabilities. In this paper, we focus on the reliability of 5G transport-network slices in EON. Specifically, we consider the problem of slicing 5G transport networks, i.e., establishing virtual networks on 5G transport, while providing dedicated protection. As dedicated protection requires a large amount of backup resources, our proposed solution incorporates two techniques to reduce backup resources: (i) bandwidth squeezing, i.e., providing a reduced protection bandwidth with respect to the original request; and (ii) survivable multi-path provisioning. We leverage the capability of EONs to fine tune spectrum allocation and adapt modulation format and Forward Error Correction (FEC) for allocating rightsize spectrum resources to network slices. Our numerical evaluation over realistic case-study network topologies quantifies the spectrum savings achieved by employing EON over traditional fixed-grid optical networks, and provides new insights on the impact of bandwidth squeezing and multi-path provisioning on spectrum utilization.

Elastic Optical Network (EON) virtualization has recently emerged as an enabling technology for 5G network slicing. A fundamental problem in EON slicing (known as Virtual Network Embedding (VNE)) is how to efficiently map a virtual network (VN) on a substrate EON characterized by elastic transponders and flexible grid. Since a number of 5G services will have strict latency requirements, the VNE problem in EONs must be solved while guaranteeing latency targets. In existing literature, latency has always been modeled as a constraint applied on the virtual links of the VN. In contrast, we argue in favor of an alternate modeling that constrains the latency of virtual paths. Constraining latency over virtual paths (vs. over virtual links) poses additional modeling and algorithmic challenges to the VNE problem, but allows us to capture end-to-end service requirements. In this paper, we first model latency in an EON by identifying the different factors that contribute to it. We formulate the VNE problem with latency guarantees as an Integer Linear Program (ILP) and propose a heuristic solution that can scale to large problem instances. We evaluated our proposed solutions using real network topologies and realistic transmission configurations under different scenarios and observed that, for a given VN request, latency constraints can be guaranteed by accepting a modest increase in network resource utilization. Latency constraints instead showed a higher impact on VN blocking ratio in dynamic scenarios.

Network operators must continuously scale the capacity of their optical backbone networks to keep apace with the proliferation of bandwidth-intensive applications. Today’s optical networks are designed to carry large traffic aggregates with coarse-grained resource allocation, and are not adequate for maximizing utilization of the expensive optical substrate. Elastic Optical Network (EON) is an emerging technology that facilitates flexible allocation of fiber spectrum by leveraging finer-grained channel spacing, tunable modulation formats and Forward Error Correction (FEC) overheads, and baud-rate assignment, to right size spectrum allocation to customer needs. Virtual Network Embedding (VNE) over EON has been a recent topic of interest due to its importance for 5G network slicing. However, the problem has not yet been addressed while simultaneously considering the full flexibility offered by an EON. In this paper, we present an optimization model that solves the VNE problem over EON when lightpath configurations can be chosen among a large (and practical) set of combinations of paths, modulation formats, FEC overheads and baud rates. The VNE over EON problem is solved in its splittable version, which significantly increases problem complexity, but is much more likely to return a feasible solution. Given the intractability of the optimal solution, we propose a heuristic to solve larger problem instances. Key results from extensive simulations are: (i) a fully-flexible VNE can save up to 60% spectrum resources compared to that where no flexibility is exploited, and (ii) solutions of our heuristic fall in more than 90% of the cases, within 5% of the optimal solution, while executing several orders of magnitude faster.

The high demand of bandwidth from multimedia applications, specially video applications which consume the great majority of the Internet bandwidth, has caused a challenge for service providers and network operators. On the one hand, the allocation of bandwidth in a fair manner for multimedia users is necessary, so that the total utility of all users is maximized for higher quality of experience. On the other hand, optimizing the utilization of network resources such as maximizing throughput is also important for network operators to reduce cost and/or maximize profits. These two requirements could potentially be conflicting; hence, achieving both at the same time is challenging, and the reason why very few previous efforts have targeted this problem. Examples include Traffic Management Using Multipath Protocol (TRUMP) and Logarithmic-Based Multipath Protocol (LBMP), both of which achieve good results but are not without shortcomings. In this paper, we propose a Price-Based Fair Bandwidth Allocation (PBFA) method by implementing an optimized sending rate adaptation technique and combining it with an intuitive investment method to optimize the feedback prices to achieve efficient and fair bandwidth allocation. The results of our performance tests, using different simulations under different network topologies, show that PBFA achieves improvements of as much as 90% in fairness, 207% in throughput, and 91% in utility compared to TRUMP

The problem of bandwidth allocation in networks is traditionally solved using distributed rate allocation algorithms under the general framework of Network Utility Maximization(NUM). Despite many advances in solving the flow assignment problem in NUM, the common but unrealistic assumption of concavity of utility functions undermines the performance of existing systems in providing satisfactory QoE to the consumers of video traffic, the utility function of which is not concave, but sigmoidal. In this work, we model the bandwidth allocation problem as a nonconvex "Sigmoidal Programming" optimization problem and use an approximation algorithm to solve it while guaranteeing a suboptimal solution. Our simulation results for video streaming over two realistic network topologies indicate improvements of at least 50% in average utility, up to 29% in fairness, and on average 14% less network capacity usage, all compared to two existing representative methods: Proportional Fair and Max-Min Fair.

Wide-spread availability of broadband internet access and the ubiquity of thin smart end devices such as smart-phones and tablets have led to a trend of moving more services away from the end devices to data centers, commonly referred to as Cloud Computing. For gaming, the stringent network bandwidth requirements of Cloud Gaming and the rapid growth of massively multiplayer online games (MMOG) call for novel content encoding and scene customization schemes to control the bit rate of the streaming video of the game scene. In this paper, a first step has been taken towards this goal by presenting a selective object encoding method to reduce the required network bandwidth and processing power without much impact on the player's quality of experience.

This paper deals with the problem of multi-agent learning of a population of players, engaged in a repeated normalform game. Assuming boundedly-rational agents, we propose a model of social learning based on trial and error, called “social reinforcement learning”. This extension of well-known Q-learning algorithm, allows players within a population to communicate and share their experiences with each other. To illustrate the effectiveness of the proposed learning algorithm, a number of simulations on the benchmark game of “Battle of Sexes” has been carried out. Results show that supplementing communication to the classical form of Q-learning, significantly improves convergence speed towards Nash equilibrium.